Let's build a RAG system - The Ollama Course

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(they have a pretty url because they are paying at least $100 per month for Discord. You help get more viewers to this channel and I can afford that too.)

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00:00:00 - Introduction to RAG and video overview
00:00:36 - Welcome to the Ollama course
00:01:00 - Tasks needed to achieve RAG
00:01:19 - Setting up the environment with Chroma
00:02:22 - Getting text into the database
00:02:48 - Building RAG system in Python
00:03:36 - Verifying data in Chroma database
00:04:39 - Building RAG system in TypeScript
00:05:20 - Querying the database and generating responses
00:06:22 - Comparing native Ollama API vs OpenAI compatible API
00:06:39 - Overview of pre-built RAG solutions
00:07:06 - Conclusion and upcoming course content
  1. Matt Williams
  2. ollama
  3. ai
  4. llm
  5. localai
  6. local rag tutorial
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Комментарии
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This was great, but... (There’s always a “but, ” isn’t there?)

I’m building a RAG system (using ollama for embedding and querying) at the moment and the hard part isn’t the RAG. It’s getting the text in the first place from PDF, MSG (including direct attachments and nested email chains), DOC[X], HTML etc. Do you have any recommendations for tooling in this arena?

bobdowling
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Just 3 days ago I had an idea to build an AI project to help me summarize 4 books a week before school starts. 2 days ago I started researching libraries, methods to get concise data without hallucinations. I was progressing but not a single video was up to date or taught what I needed. It started stressing me out. Then I found your channel. Just when I needed it you uploaded exactly what I searched for. I don't have the words to describe what a change you were to me and only because of this video I want to keep tinkering with LLM's. You are a legend who changed a 16 year old's mind about developing with AI and just gained a new patreon and I've never paid for Patreon. I look forward to watching your other videos.

MirkriBiznesmenaIgracha-lrce
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You are a legend. Love your Ollama series. Keep up the great work!

ZRowton
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Hi Matt. I always upvote your videos. It comes in mind two possible topics for future course deepenings: 1. comparison benchmark /even if qualitative) among different size of the same model, different quantization and different context window size expaining the HW respopurces trade-offs. 2: using various RAG techniques to memorize chat conversations for a sort of "long term memory" (that's indeed a very general usage). Just ideas. Thanks for all

solyarisoftware
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It's such a breeze to find your channel, a lot of influencer noise about such topics, make it challenging to find quality material.

I came in to try to answer a question, which you pointed at, at the end of the video. How do the pre-made RAG solutions compare to each other, and to the DIY one. Off course building your own, comes with the gift of knowledge of how things work, and eventually better understanding and implementing them, fixing problems when found ....etc.

My perspective if it helps: the topic has many choice points, and it can get easily overwhelming for someone of humble knowledge, and what really help is to know for someone such as yourself, why choose this over that, or what is the balance to look for? for example why choose this embedding model ?

If epub is better than PDF for the task, should we try to convert it first, to get better text content from PDFs ? Will it be helpful to rank the text extraction part before deciding to feed it to the model (what I mean is that PDFs have very much varying degrees of readability)? I'm just thinking out-loud at this point :D

Lastly, thank you and looking forward to the next video.

technologist-lxnq
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Did anyone have problems with the python scripts? I had to correct some and the requiriments.txt didn't have all the necessary packages

aristotelesfernando
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After the launch of llama 3.2 1B & 3B, this video should Skyrocket so Ollama

tecnopadre
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Video Suggestion: Table-Augmented Generation (TAG). TAG is a unified and general-purpose paradigm for answering natural language questions over databases.

MauricioDavid
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I am still using msty and having mixed results. The formating issue for data is my biggest problem right now.

startingoverpodcast
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a continuation aimed at advanced users: local vs global vs native context comparison, using graphrag and triplex - when to use which one

themaxgo
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Most excellent vid sir! Can you expamd on this by showing how to make RAG perform faster at 25 tokens per second at least, with several GB or 1000's of md files uplloaded to it please?

DrexxLaggui
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Nice videos, clearly explained in plain English. A couple of questions: why are you using different models to get the rag and non-rag respose?. And why using ollama to get the embeddings instead of leveraging the chroma embedding feature?. Thanks for sharing your expertise

karlutxo
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dear Matt, could you please make practical videos, i kept watching several videos from you but never got to the point where i get things working. please mix you videos between lecturing and practical guides step by step so we all can benifit

GhassanYousif
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When having bad results, I can"t decide if it's because of my chunking or because the data is in French.
But I can't easily find models for embedding French texts.

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Brilliant. I just subscribed. Thank You for your video series.

SoloPlax
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Would love to see further deep dive on this including hybrid keyword/semantic search and reranker for large datasets applied with an LLM via Ollama. Thanks for the great tutorial as always!

nigeldogg
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Great video, as usual. Looking forward to the RAG tools video and maybe some integrations, please

MrNevado
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1- You forgot to drink... It is important to keep hydrated.🧐
2- I prefer ready solutions especially those that give complete choices and options (Hybrid RAG with graph knowledge)💥
Thanks for the good content 🌹

HassanAllaham
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I have to jump into coding before build a rag, so actually i'm using open webui and i'm very satisfied of it 🎉

CuvelierPhilippe
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Thank you for providing playlist to learn this. Very helpful how you explain things. Always looking forward to your training series. Keep up the good work Matt!

sacredgeometry