Video

Hey James, First of all, thank you for all the content you put out there, it is quite helpful. I am trying to figure out how this would fit into a chatbot pipeline. Upon further thinking, Semantic Router will be a replacement for OpenAI (to put an example) way of defining which tool to use (platform.openai.com/docs/guides/function-calling/understanding-token-usage) i.e. saving some tokens and perhaps more accuracy. However, we will still need to invoke the LLM with one function schema (after the decision of which tool to use was made). In a nutshell, a semantic router is a replacement of the prompt with multiple schemas that OpenAI uses under the hood, right?

Unfortunately, the semantic router has removed this feature, or refactored it in some way.

Learning this at udemy. Finished one chapter. Saw end to end. Liked langsmith. This is a good foundation of machine meaningful graph.

is cohere free?

SmythOS facilitates efficient multi-tasking with AI applications, making it ideal for handling complex models and large datasets. Its performance optimization helps in minimizing downtime and improving overall system reliability.

27:17 this is soooo helpful

Hey I had a question how could I do this I multiple languages

Awesome, this is the video I need. I'll watch this carefully. I've tried langgraph, and it felt really complex to manage the tools and workflows. Giving the description to trigger the tool is hard if you have similar steps in your workflow. Making the caller to use the exact response from the tool was also really hard. And thinking of scalability, like adding steps in between in the long term in a project with multiple people, feels it's going to be hard. I tried also adding multi agents by passing a workflow as a node, and again, it was a bit hard to make everything work as expected. I was just following a simple flowchart with a few steps and forks. Even though I was able to make it work, it felt like I was doing everything so wrong. Anyway, I hope your video put some lights on my way to understand this better

Even after 3 years I haven't found such an amazing, easy to follow tutorial series on this topic. I really loved it James. Thank you!❤

Thank You. Very timely and useful !

How can I use llama3.1 to achieve this?

Excellent description. I have thousands of images with design objects on a Mac FileMaker server, which can connect to gpt-4o or similar AI-model. I am interested to hear your opinion upon analysing this images according to a JSON file with some few hundreds design taxonomies (category name and description). You are welcome to contact me directly.

Hi James, I love your miniseries on semantic router. I am really looking for usecases for this. Have you been able to collect some common use cases from your users for inspiration? Would be awesome and super helpful if you made a video on this subject. Thank you 🙏

Have you tried to use this approach to build a team of agents that collaborate to solve a specific task from multiple different specific perspectives?

Really nice job! Really wanna have a walk through on how to stream the final answer as well.

Super cool project

Has anyone tried it with cheaper LLM(s)? With which and how did you like the result?

I tried this approach to build agent for my use case with bedrock LLM, instead of Open Ai model i just introduced Bedrock LLM and keep all remaining things same but facing issue as : Error raised by bedrock services : messages : final assistant content can not end up with white trailing space May be this error is due to custom scratchpad can you guide me to resolve this error?

Openai doesnot provide free api keys anymore so what alternative can i use?

How is it different than DSPy ?

Great tutorial as usual James!

.bind_tools is not supported on HuggingFace

Very nice content James ! Just discovering langgraph I have a question : in what which is It different from crew AI ?

Thanks for this James. How do I switch from OPENAI KEY to GROQ KEY? Or even using Ollama?

btw, what about using the semantic-router with langGraph ?

I love LangGraph as well, looking forward solidifying my knowledge. Cheers James. ps . Would you be interested doing video/research on meta-data creation from a hierarchical perspective : Parent <> children ? I think we could greatly enhance RAG quality if we build a hierarchical structure of meta-data. Problem here is how to create and label this data. From my testing with "GliNER" it doesn't nearly capture what id like and needs fine-tuning (but maybe we could train it on our client specific data..). Cheers!!

Can we load a dataset from our private cloud?(This data I don't want to upload to hugging face) I don't find any examples

Thanks for this video, great as usual. One thing I don't understand is what is breaking the "loop"/further gathering of information. Is it when the "Oracle" thinks it has "plenty of information" as stated in the system prompt? Or when does it stop?

Great Content !!👏👏👏👏

Your presentation is so good, I use it to listen to it in the background when I'm coding or doing other tasks. Then, when I watch the tutorial for good I find that I follow much better than if I would watch and follow right away.

Can you please also make a video about human in the loop in depth. Thank you

I always enjoy your video. I have a better understanding of LangGraph. As with your video, all of the demonstrations I have watched are one sentence or requests to the LLM. Here are two examples of Requests. I'm interested in what the best type of dog is for child. My daughter is five years old. We live in Minnesota, which is quite cold, so we need a dog that is good for cold weather. Please provide me with a few suggestions. I am deeply intrigued by the various reasoning approaches to building my report writer research agent. I have discovered two approaches: Tree of Thought and Chain of Thought. I am eager to gain a good understanding of each. Please provide me with a report that defines each strength and weakness for each. Make a recommendation as to which one would be good for building my research agent.

Thanks a lot for sharing. The topic looks complex but you made it as neat as possible! The combination of video, code and article is really helpful. Video is good in the sense that it is more interactive, but I do need the article to get a more straight-forward sense of the whole idea. Some questions: 1) "input": "tell me something interesting about dogs" became 'interesting facts about dogs' in the output. Is this the result of the step langchain_core.tools? 2) in rag_search_filter, top_k=6; in rag_search, top_k=2. Does this mean return the top_k answers? I asked this because one was doing search within one article, the other was searching in indexes (I assume the index was one article one index?) 3) graph.add_node("oracle", run_oracle) graph.add_node("rag_search_filter", run_tool) graph.add_node("rag_search", run_tool) graph.add_node("fetch_arxiv", run_tool) graph.add_node("web_search", run_tool) graph.add_node("final_answer", run_tool) Will all of them be forced to execute? I see from the result rag_search, web_search, final_answer were invoked. Then how does this graph determines which tools to invoke? Order seems to matter too. The subsequent tools will be affected by the previous tools' results, right? Then how is order decided?

In your opinion, what situations would you stick to Langchain VS Langgraph? Or should we now always use Langgraph?

Simple and Efficient

📌 Code: colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/langgraph/01-gpt-4o-research-agent.ipynb 📖 Article: www.pinecone.io/learn/langgraph-research-agent/

Looking forward to this one. Especially with the new features LangChain has been bringing out in the past weeks. Exciting times to be building AI applications, for sure!

Thanks for this content

is levenshtein similar to dynamic time warping?

thank you yet again !!!!

clear as mud

Hi can you convert end to end project

i am getting this error while calling evaluate function AttributeError: 'DataFrame' object has no attribute 'rename_columns'

Super informative. Thank you so much!!!

Nothing new.

This is great - any more resources on recommendations on adding metadata (ie. title) to embeddings? Can't decide if I should put them in the embeddings vs. having a more traditional index using something like hybrid search... Also in addition to title, what about dates, author etc... I could imagine each chunk being 50% of this metadata. Wouldn't that compromise the embedding?

I've been using a tool unstructured to split my documents into known sections (ie. title, abstract, pararaphs) - it can do the splitting. Do you think having these sentences apriori is helpful to chunking or it's better to just feed plaintex to the chunking strat and let it do all the grouping/separations etc...

I want to get answers from a table in the image, how to do that?

You need to define your llm in step 2 of asking the model directly.... llm = HuggingFacePredictor( endpoint_name="flan-t5-demo" # Use the name of your deployed endpoint )

Thanks a ton James!!