Routing with LangChain - Basics - Semantic Routing vs. LLM Classifier

In this Video I will show you why you want to perform routing in langchain and how you can do routing. We will explore Semantic Routing (cosine similarty) vs LLM based Classifier as Router.

Timestamps
0:00 Intro to routing
0:18 Semantic Routing
4:13 LLM based Classifier

[databasemadness]

Short, crisp and clear! Another amazing video

[yazanrisheh]

Please continue uploading these. I was following your old LangChain videos before LCEL and now I still find LCEL a bit confusing. Can you make a video on what exactly is runnable and the differences? RunnableLamda, RunnablePassthrough and any other runnables etc...

[AbinBinu-wj]

just what i wanted, dude awesome, i m gonna sub u now, awesome content

[nintendo]

This is the clearest video out there about routing with LangChain. Amazing, thank you!

Would you say it is possible to fine-tune a model to become better at classifying so that I can use your second method (LLM-based classification) on top of that fine-tuned model?

[StudioSeenDesigns]

How do you think this pairs up against something like Langgraph?

[seththunder]

In the part about cosine similarity, I have 2 questions:

1) Why did you use cosine similarity and not dot product for instance or any other method?
2) Shouldn't be there a certain threshold value that u can put to determine how close are they?

[yazanrisheh]

Since langchain is alwyas changing, can you please show us at the start of every video what version you're using? Thanks

[rakeshkumarrout]

this is awsome how can we add vectorstore to it? and can you modify this according to this output code "self_query_retrieval_chain = (
{"context": itemgetter("question") | self_query_retriever, "question": itemgetter("question")}
|
| {"response": rag_prompt | chat_model, "context": itemgetter("context")}
)"