LangChain Expression Language (LCEL) Explained!

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The LangChain Expression Language (LCEL) is an abstraction of some interesting Python concepts into a format that enables a "minimalist" code layer for building chains of LangChain components.
LCEL comes with strong support for:
1. Superfast development of chains.
2. Advanced features such as streaming, async, parallel execution, and more.
3. Easy integration with LangSmith and LangServe.
To understand LCEL we'll dive into its syntax, how it works under the hood, and a few examples of how it is used alongside RunnableParallel, RunnablePassthrough, and RunnableLambda objects.
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00:00 LangChain Expression Language LCEL
01:06 Getting Started with LCEL
06:11 How LCEL Pipe Operator Works
12:21 Using LangChain Expression Language
14:16 LCEL Runnables
19:36 LCEL Runnable Lambdas
23:36 Pros and Cons of LCEL
#ai #artificialintelligence #langchain #nlp #anthropic

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Опубликовано:

26 июн 2024

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Комментарии : 54

@izarniz 5 дней назад

I bought a course on Udemy, which claimed to be the complete guide to Langchain. I understood more about langchain from this video than the entire course. Thank you so much for the content. You really are a good teacher :)

@dikshyakasaju7541 6 месяцев назад

Recently, I've observed widespread use of Pipe operator, and this video provided a clear explanation of its purpose and functionality. Once again, it was incredibly helpful!

@jamesbriggs 6 месяцев назад

glad to hear it!

@pavellegkodymov4295 Месяц назад

James, thank you so much, you've made this "scary" topic digestible converting it to step by step in depth practical guide. Thank a lot for your work!

@trefoilanalytics6644 5 месяцев назад

Pipes are a powerful tool for clearly expressing a sequence of multiple operations. They are used for long time in R. People coming from R to Python will be happy with the pipe operator.

@JunYamog 19 дней назад

Thanks for this more in depth explanation. Coming from Scala, LCEL makes sense for me.

@zd676 4 месяца назад

Better explanation than the video from Langchain! Awesome work!

@codekiln 26 дней назад

Great video! I’d love a follow up with custom data formats or output formats

@jonm691 22 дня назад

Great video James. Thanks

@user-wu9ju7ti5j 6 месяцев назад

Thank you James, very clear, looking to experiment with Remote Runnables etc.

@Cdaprod 6 месяцев назад

I absolutely LOVE lcel

@alivecoding4995 6 месяцев назад

That's a great topic! Thanks James :)

@RichardBurgmann 5 месяцев назад

Great video and explanation. I think the pipe operator is a solution in search of a problem. Might be usefull in some contexts.

@snsa_kscc 6 месяцев назад

Happy birthday, bro! 🎂

@jamesbriggs 6 месяцев назад

appreciate it!

@sethhavens1574 5 месяцев назад

Confused by pipes! Oh the kids these days 😉 another great vid, thanks dude (you should try some awk and sed one day for fun)

@programwithpradhan 6 месяцев назад

Many many happy returns of the day Bro how you come up with these techniques,do you follow some specific blog or anything could you please tell me

@codingcrashcourses8533 6 месяцев назад

I am still not sure, if I like this or not. It´s the official go-to way now, but I still prefer the Chain Interface. For RetrievalQA you had this nice Chain, now you have to do everything on your own. I still miss some easy to use and clean examples with the LCEL for more advanced usecases.

@greyshopleskin2315 5 месяцев назад

Old chains like RetrievalQA are compatible with the pipe stuff. Both kind of chains inherit from the Chain class (i guess). You can do: my_pipe_chain = blah | blah qa_chain = RetrievalQA(…) awesome_chain = my_pipe_chain | qa_chain I have to implement new features in a project and we were using class based chains and were doing some crazy shit. You don’t want to see that stuff. I we had used pipe chains, it would be much easier to refactor this. And if you can just use the chains as is, good. But we needed to change lots of stuff and it was confusing, all those clases which inherits and delegates in a dozen ones… A big problem is that we didn’t really knew everything those chains were doing, so know Im converting them to pipe chains and I have to read a lot of code, jumping around. More precisely, one of my biggest problems is reproducing everything the ConversationalRetrievalQA chain does

@SonGoku-pc7jl 5 месяцев назад

thanks! amazing xD

@experter_analyser 6 месяцев назад

Thank you. Can this be done using palm API

@sowmyancs 6 месяцев назад

Great content. It works with Agent, in my case I need to use AgentExecutor instead of Agent, agent_executor = AgentExecutor(agent=agent_chain, memory=memory, verbose=True, tools=tool_list,return_intermediate_steps=False) Look like AgentExecutor is not streaming with LCEL. Any ideas?

@hiranga 4 месяца назад

@jamesbriggs Great tute! James, what's the right way of using a LCEL chain as a Tool inside an Agent?

@NadaaTaiyab 6 месяцев назад

How do you add a variable for metadata filtering when using LCEL. I just can't figure it out. And how do you get verbose mode so that you can see the all the prompts and steps taking place?

@AbhayMishra-dz3xs Месяц назад

Same my issue , Did you get solution of it ??

@joffreylemery6414 6 месяцев назад

Please more and more of this content ! I recently spend one day trying to implenent SelfQuerying with Pinecone and langchain. But it is a fail... Impossible to filter on metadata. And the doc from pinecone on the Index is not super esy. If you want to do more very pedagogical video like this on the notion of Index (and thus self querying) in order to avoir using Langchain to push in Pinecone, it would be incredible !

@leobeeson1 5 месяцев назад

I gave up on LangChain's SelfQueryRetreiver a few months ago and basically use three patterns: 1) a postgres database with a class that does `user utterance`->`natural language query`->`sql query` translation to select a subset of documents, 2) an on-the-fly embedding + vector similarity estimator on the subset of documents, and 3) control-flow operators where the LLM chooses from a set of enums (for categorical matching) or estimates a coeficient (for thresholding). More importantly, with the first pattern, you can now answer questions like "What are the top 5 complains from customers that scored us with low NPS?" (and then generate summary statistics passing the results to pandas), which is completely impossible to answer via RAG or any other form of embedding-based search. This guy has a series worth checking out on ho9w to build a Postgres Data Analytics AI Agent starting with this video: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-jmDMusirPKA.html

@joffreylemery6414 5 месяцев назад

@@leobeeson1 Let me tell you that you made my day ! I will check that with big interest!!

@stephanembatchou5300 6 месяцев назад

This is great actually. If you are familiar with R statistical language you will easily grasp the pipe approach. People just need to get used to this new paradigm.

@jamesbriggs 6 месяцев назад

yeah I think it's pretty interesting, it's weird when you're coming from Python but once you start using it it seems nice - for now I think it mainly needs better integration with agents, idk if that is planned or not but most of the time I'm using LangChain for agents

@mattie3875 6 месяцев назад

One day this might catch up to the usability of chatsnack, but hard to see if this is the right direction?

@b1naryc0de01 3 месяца назад

I don't mind the syntax too much... It's not pythonic but I'm used to it because it reminds me of R tidyverse pipelines. What I can't really find/understand is what about incorporating history in chats, and implementing callbacks using LCEL? I haven't found understandable documentation for that. Initially it appears in the process of making a clean implementation for chains they have made adding history (like ConversationBufferMemory) pretty complicated and it wasn't previously. Oh and thanks for the in depth walk through of how they overload that pipe operator to use it in python very good walk though on that!

@jamesbriggs 3 месяца назад

I included chat history with LCEL (pretty manual) in this walkthrough if it helps :) ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-rbzYZLfQbAM.html

@adambenari3944 6 месяцев назад

Hey James, nice video. I recently refactored some code to use LCEL and I enjoyed the process. Any chance you have a video lined up around setting up benchmarks for a system in production. And then evaluating potential changes before they go live? Especially for one that uses RAG, thanks.

@pec8377 Месяц назад

How would you use conversation and LCEL together ? Cheers

@alivecoding4995 2 месяца назад

So the pipeline operator can't forward unconsumed inputs automatically further downstream?

@alberjumper 6 месяцев назад

The problem is not that is "not Pythonic", the problem is that it is not READABLE. We are sacrificing readability, which is always important, specially important when building something that can be conceptually challenging, as an LLM pipeline is.

@SeiyaRyuuguuin 6 месяцев назад

Hey James, new to the AI creation space and my goal is to try my hand at creating a AI V Tuber with personality, could you please point me in the best direction to follow out of all the videos you have to help me follow this goal. Your videos are great but hard to pick one to follow for it. Thanks!

@JReuben111 6 месяцев назад

pipe operator and RunnableParallel - reminds me of Apache Beam python API. - good to see they are not putting "Zen of Python" dogmaticism before AI innovation ! LangChain docs have completely changed since a year ago - this field is moving so fast !

@user-wr4yl7tx3w 2 месяца назад

i was wondering where these pipes suddenly came from...

@zacboyles1396 6 месяцев назад

If you ask langchain chat anything slightly more advanced than the most basic LCEL questions, it spits out incorrect code. If you neatly organize all the LCEL examples for context, GPT4 produces worthless code responses. If you add in all of the runnable source code and give that a whirl, GPT4 is still unable to build simple prompt templates, let alone work output parsers into the mix, forget tools. Nothing in recent memory has made me so angry than the Python implementation of LCEL and I tried so hard to like it. The focus on LCEL seems to have made the entire library suffer as the general langchain examples are sporadically functional and it’s difficult understanding the recommended approach to use certain functionality over their updates the past 6 months.

@5thbatman 5 месяцев назад

apache beam had the same thing...

@thedoctor5478 6 месяцев назад

Sounds like a lesser (since it's langchain) ripoff of LMQL. Langchain can do a lot but working on its codebase is one of the worst experiencing working on a repo I've ever had. It's unmanageable for a production environment. LMQL is awesome btw.

@jamesbriggs 6 месяцев назад

nice I haven't dived into LMQL yet - will check it out soon :)

@dinupavithran 6 месяцев назад

LangChain team answered this in negative (ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-9M8x485j_lU.htmlsi=kkHWKA9VcClwXfxU&t=2396). According to them LMQL is focused on instructions to LLM where as LCEL is for composing of processing outside LLM.

@robxmccarthy 6 месяцев назад

I agree, langchain was very helpful early on when it was just a simple wrapper for common LLM functions. However, it has just grown more and more complex without a clear underlying principle.

@zacboyles1396 6 месяцев назад

Have you tried Marvin or Instructor? They both do what LMQL does but they integrate with Python much better. Neither has that LMQL playground but I’m not sure the benefit of that over using Marvin/instructor in a Jupyter notebook.

@thedoctor5478 6 месяцев назад

Instructor is on my list of things to add to my system. It seems odd to hear it integrates with Python better since LMQL is scope-aware when you include it in a docstring with the LMQL decorator on a function. @@zacboyles1396

@ew3995 6 месяцев назад

i really dislike langchain because i think its just a massivw unreliable regex hack, does this make langchain more useable/reliable?

@codingcrashcourses8533 6 месяцев назад

It´s not a regex hack. It just overloads the __OR__ operator. James even explains it.

@wufei523 6 месяцев назад

thank you. langchain documentation sucks. i was really confusing until i watched this videos

@mitast 4 месяца назад

The pipes are the most confusing thing ever created. Thanks for the video - it explains them in a great way, but it doesn't make them more pleasant. This is totally unneeded and it is not even a syntax sugar, because it doesn't make the syntax more easy and it doesn't make the code more understandable or reduce the boilerplate. Exactly the opposite - you have to learn totally new concept with no any need for it.

@gorgolyt 3 месяца назад

LangChain was already a questionable abstraction. Now they've built an abstraction on top of the abstraction. 🤔 If you're okay at Python and understand anything about LLMs, I think this library is best avoided, especially its core abstractions like chains, agents, etcetera. If you want some convenient interfaces to components like LLMs or vector databases then go ahead and use what you need. But beyond that, it causes much more work than it avoids. Especially as the documentation is pretty poor. They haven't even bothered with docstrings for the main classes.