LLM - Day 5 - RAG a Intro

#rag #letslearnrag #ragintamil

Retrievel Augment Generation

3 different Topics
when we need RAG ? You have Private Data Source Eg., கம்பெனி குள்ள இருக்கிற Confluence dataஓ or documents ( these won't be available to public )

The above funcationality should be available for my private data also. Eg., In my company leave policy.
This is resolved by RAG.

RAG Architecture

3 parts are there.
Pre-processing steps --> RAG , ready பண்றதுக்கு முன்னாடி நான் இதெல்லாம் செய்யணும். இந்த diagramல dash dash இருக்குல்ல அதுதான்
Before pre-processing , let me draw a graph.

Orange x pointகு closeசா இருக்கிற point சொல்லுங்க. So [8,9,5] . How you told these point , how you processed ? Going to take distance of all points then sort. This is what we are going to get in Pre-processing.
Blue x are the chunks. what is Chunks ? A part. A part of what ? its the documents.
Eg., 500 character is a chunck. C1 . Then again C2 ... C5 etc , entire documents.எல்லா documentமெண்ட்டையும் துண்டு துண்டா உடைக்கும்.

Now all chunks C1 C2 etc is given to MODEL which is a EMBEDDING Model.
This EMBEDDING Model will give POINT.
Eg., You are very good food industry.
ஒரு ஒரு chunkகும் ஒரு ஒரு point. Related items occur closer together.
These points will be put in graph. You are creating a cluster like , with similar topics together.
EMBEDDING Model is nothing but LLM also it contains Transformer.
In simple -> நீ ஒரு TEXT குடுக்குற நான் ஒரு POINT கொடுக்கிறேன், அவ்வளவுதான் indirectly a Knowledge source.
So they are in MULTIDIMENTIONAL SPACE.
For a single chunk it will be 786D ( it means 786 different points ). To create this dimention , VECTOR DATABASE.
Vector & Embedding are almost similar. ( Here Vector will have direction in addition ).
Before giving to LLM , it will be chunked.
Vector DB eg., softwares below

One is Distance , It will give shortest distance algorithm like ( euclidean algorithm , manhattan algorithm , etc ).
Another is Cosine similarity --> Lesser the angle , these points are more similar or closer to each other. , also its SEMENTIC. what is Sementic ? Occur close together. Eg., Rice and Biriyani , but indirectly they are close together. In simple , how similar these two points are.

Contexts --> Actual data from Vector DB. (this is nothing but the meta data).
Query + context --> you have right now.
Now with this AUGUMENT , its nothing but "PROMPT".
This Prompt will have "Query + context" then format , etc etc all condition. இந்த processசஸ ரெடி பண்றதுக்கு பேருதான் Augument.
Now we will give this to LLM. Eg., chatgpt , gemini.
For this LLM , its a query.
How the dynamic doc are handled ? Recreate or upend. Depends on the documents.

Notes

"Confluence data" or data confluence means தரவு சங்கமம் (Tharavu Sangamam) — the combining of different data sources, streams, or types into one single place or system.
obsidian graph
https://plotly.com/python/3d-scatter-plots/
Augument means COMBINING.
Query is Orange X and Blue is Chunks. Along with the point , a meta data will be stored . DATA ABOVE THE POINT .