llm-zoomcamp

Tasks related to LLM Zoomcamp from June 2024

1.1 Introduction to LLM and RAG

LLM

RAG

RAG architecture

Course outcome

1.2 Preparing the Environment

Installing libraries

Alternative: installing anaconda or miniconda

1.3 Retrieval

We will use the search engine we build in the build-your-own-search-engine workshop: minsearch

Indexing the documents

Peforming the search

1.4 1.4 Generation with OpenAI

Invoking OpenAI API

Building the prompt

Getting the answer

If you don't want to use a service, you can run an LLM locally refer to module 2 for more details.

In particular, check "2.7 Ollama - Running LLMs on a CPU" - it can work with OpenAI API, so to make the example from 1.4 work locally, you only need to change a few lines of code.

1.4.2 OpenAI API Alternatives

1.5 Cleaned RAG flow

Cleaning the code we wrote so far

Making it modular

1.6 Searching with ElasticSearch

Run ElasticSearch with Docker

Index the documents

Replace MinSearch with ElasticSearch

Running ElasticSearch:

docker run -it
--rm
--name elasticsearch
-p 9200:9200
-p 9300:9300
-e "discovery.type=single-node"
-e "xpack.security.enabled=false"
docker.elastic.co/elasticsearch/elasticsearch:8.4.3

Index settings:

{ "settings": { "number_of_shards": 1, "number_of_replicas": 0 }, "mappings": { "properties": { "text": {"type": "text"}, "section": {"type": "text"}, "question": {"type": "text"}, "course": {"type": "keyword"} } } } Query:

{ "size": 5, "query": { "bool": { "must": { "multi_match": { "query": query, "fields": ["question^3", "text", "section"], "type": "best_fields" } }, "filter": { "term": { "course": "data-engineering-zoomcamp" } } } } } We use "type": "best_fields". You can read more about different types of multi_match search in elastic-search.md.