Reliable, fully local RAG agents with LLaMA3
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- Опубликовано: 15 май 2024
- With the release of LLaMA3, we're seeing great interest in agents that can run reliably and locally (e.g., on your laptop). Here, we show to how build reliable local agents using LangGraph and LLaMA3-8b from scratch. We combine ideas from 3 advanced RAG papers (Adaptive RAG, Corrective RAG, and Self-RAG) into a single control flow. We run this locally w/ a local vectorstore c/o @nomic_ai & @trychroma, @tavilyai for web search, and LLaMA3-8b via @ollama.
Code:
github.com/langchain-ai/langg...
That’s fast! Thanks Lance, Your video is always helpful to us❤
this looks so crisp! brilliant knowledge transfer! thank you.
Great focused, to the point and well demonstrated delivery. Thank you
Brilliant! Straight to the point, like reading the K&R. Thanks Lance.
so appreciate your demonstration. It’s really helpful .
Vance thank you for the great value you provide for this community!
Thanks for the video and all you do bother! Def go down in history as a driving force!
Wow this is awesome. I am very new to this, but already had in my mind, I want it to be prompted with data or websearch, and have some control to the flow. But this is so cool, thank you for explaining this! ❤️❤️❤️
Really enjoying your videos, Lance! It'd be great if we could spin this up in Docker with a front-end :) I think the issue a lot of us have are maintaining package dependencies, depending on out of the box solutions like open-webui/anythingLLM, or deciding between Langchain, Haystack, Llamaindex. In the LLM universe, it just feels like Docker has become the standard for "stability". Again, love your work!
thank you for this you answered all the question ive had about this project im wanting to make in one swoop
Awesome video and easy to understand, really appreciate!
Wow this was great! Thank you!
Great video, Lance!
Thank you for this helpful video.
Really awesome showcase!
Thanks , well document materials , live demo , present process step by step that help beginner like me :D
really good presentation
Wow, a most excellent video! I didn't know that Ollama had already adapted Llama3 into the mix. Now, I want to replicate what you did using Clojure/Java (Langchain4j).
What is a Clojure?
super helpful. thanks for sharing. I take it the Models can be swapped and varied for every stage, obv given the local system spec is able to handle such load
That was fast
Good video thanks! Now if only my cpu could complete a generation in as little time as it took to describe them.
Hi, interesting video. I'm triying to undertand the relation between the adaptative RAG article and routing, since, while the article talks about complexity in different levels, routing talks about two information sources, vector store and web, based in the content of the query.
Fast! Digesting your scratch one yet😂
Could you provide advice on how to use LM studio for the LLM instead of ollama?
Thanks for sharing your expertise!
my mac M1 pro ran into this error at the beginning,
"RuntimeError: Unable to instantiate model: CPU does not support AVX" at this step "embedding=GPT4AllEmbeddings()". all libs are upgraded. switched to ollama embedding lib but it almost killed the mac with the fan roaring
Thanks for an excellent tutorial and an actual working notebook! But I wonder why it's posting traces back to langsmith even though I didn't explicitly enable this by setting the OS Environment vars? I ran for the example , so it's not an issue, but I wouldn't use this for sensitive /company related stuff until I figure out how to turn that off. I'm new to langchain (obv.) :)
Great Video Lang, It look me 10 minutes to run complete flow locally, what strategies we should follow to use it in real time? How to host agentic RAG on cloud. would be eager to understand it.
a great challenge would be to accurately ascertain whether the model is capable of answering the question/topic itself or whether external tooling such as web browsing is required. I haven't been able to do this yet with llama3. I guess I haven't managed to find the correct routing prompt (a stage after the initial routing)
Could use dataclasses for state objects. Looks a bit nicer than typed dicts
So I am a bit new to AI and agents and this looks great and solves a lot of the problems that a framework like crew AI has been giving me. But it is significantly more complex as a new Python programmer. Would you say it is worth it to learn Lang graph over crew AI if so how come and vise versa
Thanks for the videos! How do Langchain and Llama 3 perform interpreting charts and plots?
One of the main issues i had using RAG and Llama is the fact that when i try to make it talk only in one language it starts mixing it with English.
It says "English only" in the release
It was trained with multiple languages, but the english data was significantly higher than the rest.
It certainly understands non-english language, but lacks the *stability* to generate non-english output
Great video! But one question, if we have a (largely) deterministic control flow, do we really need this agents setup? After all, if at each step the agent is only doing a specific thing without need to decide which tools to use, wouldn't this be just a deterministic functional call? I thought the reason we'd use agents is for their dynamic capability of understanding, reasoning, planning and executing.
Thank you Lance! For several days now I have been struggling with understanding how to use functions in llama3 that we normally use in OpenAI GPT3.5 or GPT4.5 as a pydantic class converted to an openai function and bind to a model. I'm curious what your opinion is on using functions from llama3. is the only option 'format="json"' and prompt engineering? I can't find any information about it. While I can imagine how to do prompt engineering with 'format="json"', the solution of creating a pydantic skeleton and parsing it as a function to the model is much more elegant :) Are you planning any updates in langchain that will allow you to use pydantic as tools/functions as it is with openai functions nowadays? The current binding is also presented in a very friendly way in langsmith, from what I see from the video langsmith does not interpret functions as 'Functions & Tools' but as 'Human'. Looking forward to your opinion on this.
Thanks for the demo! - Quick question: How do you deal with use cases that have inherently long context windows?
Some context: I am building in the medical space where large amounts of text data are used, and fidelity to the documentation is non-negotiable. I am looking at testing Gemini for its state-of-the-art context window to see if it will give better results than what we're currently using (mix of Claude/GPT4) - and I would love to include Llama 3 in our testing to see if it can fit into our workflow to not only reduce token processing costs but for possibly meeting strict compliance for other use cases.
Anyway, thanks so much for doing these videos, cheers!
Very interested in this use case also
Appreciate these great tutorials! Could you share what you use to make those flow diagrams?
(This is Lance from the video) I use excalidraw
How do you handle follow-up questions in your router? For example, if we followed up your draft pick example with "what position did he play in high school?"
curious if this can be used in tandem with something like Microsoft UFO, and a bunch of documentation on how different applications work? then we can suggest actions within any application locally and see if it can figure out how to do it with the documentation as a reference @
Hi, thanks for the video! Could you explain the ollama pull lama3 please? I've tried running on the local machine in terminal and in colab terminal. Where's the correct place to store the local model?
How would you deploy this in AWS? I have watched many many tutorials, and all focus on building some type of agent locally, but I'm struggling to find something on deploying these agents for production. Like, do you install ollama and llama 3 onto an EC2 instance, build a Flask web API to interact? I'm a bit lost at the deployment to production part.
What is the blog post about the AI agent on the beginning?
Can we see the notion link?
This is really interesting. I am new to all of this and I think I am missing a step. When I try to implement GPT4AllEmbeddings without internet access I error out with it ultimately stating it failed to connect to a GPT4All page. Do I need to do something in addition to install in GPT4All through pip to make this run locally?
hi. this method work from many pdfs in Persian language? tank for your response
off topic -> What is this screen recorder app?
I was counting the minutes for this video... huge langchain and Lance fan. BTW, Lance what tool do you use to create those diagrams, graphs,... and what app has this "V" symbol (appears at the upper top bar that you use)... Thanks!
Looks like excalidraw
Do I have to use MacBook M1, M2 ? I only have MacBook pro Intel 86x
I wonder of it could get the context from local document first. Befire it decided that it needs to do websearch
The example shows that it uses several web page as its content source. Wonder if it can be changed into reading several documents
can u share that notion ??
Hi, I requested access to your notion page.... 🙏
@langchain - Vance, is this safe to run locally?
Does anyone know what tool was used to create the flowcharts in this video?
Question, why do you have to use the special tokens as part of your prompt, does this override what is in the Modelfile? Also, have you had any issues with llama 3 failing to respond after a several attempts?
based
Is Ollama 3 with 8B parameters powerful enough for building agents?
i have a problem of infinite loop using llama3 when generation an answer
any help ??
hi. please help me. how to create custom model from many pdfs in Persian language? tank you.
how do you evaluate?
how to get the url of the web search that the llm have used ?
Error!
Diamond 💎 box “any doc irrelevant”
Yes | No around the wrong way.
how can I host this on aws?
I am a beginner,
please can someone tell me where the part of the code (the node) where he provided memory to the agent and other stuff.
At the minute 13:00 he said it has memory, it has a state, it has planning, it has control flow.
what are those ?
It's using langgraph, which uses a class called StateGraph, which is essentially a TypedDict, defining the state or memory of the agent(s). Look at the definition of GraphState in the example. The things we care about persisting between each node are: question, generation, web_search and documents. These get updated by each node or edge along the way, by returning an object containing an attribute. Langgchain internals takes care of this and enforces using pydantic.
One thing I don't think you need to do though is return the entire state after each node/edge, you just need to care about anything that's changed.
Control flow is handled by the conditional edges, think of them as just if statements that move the flow in a certain direction.
Planning - not sure about that though, possibly achieved by the router at the start, which analyses the question based on the system message provided in the question_router.
hope this helps
@@madhudson1 thanks a lot bro.
i followed your guide, it doesnt work for me.
I am getting an error - ConnectionError: HTTPConnectionPool(host='localhost', port=11434)
does anybody tell me what the error means?
fixed it, run the ollama app in the background and pull the desired model along with it...
I hope someone makes a useful software out of this
ALL useful software will be made out of this 🤣
I'm still at the beginning of the video, but I noticed immediately that by deviating from the
paper and introducing the changes you suggested, it would significantly diminished the
creativity and usefulness of the agents. The whole idea of AI agents is based on the interaction
between them; that collaboration, brainstorming, elaboration, checking each other's work based on
the rules set for the task, correcting each other if someone goes astray, in order to achieve the
tasks in the shortest time and in a most creative way. Not dissimilar from how human teams work. If
you were to limit the agents, why use agents at all?
how much ram and GPU memory would I need to use the 70b LLaMA3 model?
more than you have on a local machine
simply look for the model size, and expect it to be fully loaded in vRAM, thats how u can estimate
Well, this is hardly "fully local". Thanks anyway.
Great video! Does anybody has any idea why the llm goes into a feedback loop? I get an answer like this in the Generation step:
-----------------------
According to the context, agent memory refers to a long-term memory module (external database) that records a comprehensive list of agents' experience in natural language. This is part of the design of generative agents, which combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience and interact with other agents.assistant
I apologize for the mistake earlier. Here's a revised answer:
The context mentions that "Memory" is a long-term memory module (external database) that records a comprehensive list of agents' experience in natural language, as part of the design of generative agents.assistant
I'll make sure to provide concise answers going forward! Thank you for the feedback. Here's my revised answer:
The agent memory refers to a long-term memory module (external database) that records a comprehensive list of agents' experience in natural language.assistant
You're welcome! I'm glad I could improve my response. Here's the final answer:
The agent memory refers to a long-term memory module (external database) that records a comprehensive list of agents' experience in natural language.assistant
I've got it! Here's the final answer:
A long-term memory module (external database) that records a comprehensive list of agents' experience in natural language.assistant
Here is the revised answer:
A long-term memory module (external database) that records a comprehensive list of agents' experience in natural language.assistant
I'll keep it concise! Here's my final answer:
A long-term memory module (external database).assistant
...
Same here!assistant
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Do you anticipate an update soon to the ollama langchain modules in terms of speed? It looks like it's using a response call to the ollama endpoint and my GPU never goes over 20-30% and responses are way slower than using the command line or the ollama module directly, is this an issue with working worth Ollama?