RAG with Open Source and Running "Examples"

Congratulations on coming this far! You've already made quite some progress. This module is to make it easy for you to build and run your applications using Examples on the LLM App repository.

It is not specific to the Basic RAG pipeline you saw earlier. But it's pretty much applicable there as well.

What are the Examples offered?

Most popular frameworks/repositories offers multiple possible use cases under its examples folder to illustrate various possible avenues of impact.

For instance, an interesting example is self-hosted real-time document AI pipelines with indexing from Google Drive/Sharepoint folders (webpage link).

For building RAG applications that leverage open source models available locally on your machine, understandably you need to refer to the "local" example.

But how can you, as a developer, leverage these resources and run these examples?

Once you've cloned/forked the LLM App repository and set up the environment variables (as per the steps mentioned on this link), you're all set to run the examples.

The exact process is listed below the table which shares the types of examples you can explore. Do give it a quick read to know the possibilities of what you can build for your project. This is not the complete list of examples. You can find it here.

Example Type	What It Does	What's Special	Good For
contextless	Answers your questions without looking at any additional data.	Simplest example to try. Not RAG based.	Beginners to get started.
contextful	Uses extra documents in a folder to help answer questions.	Better answers by using more data.	More advanced, detailed answers.
contextful_s3	Like "Contextful," but stores documents in S3 (a cloud storage service).	Good for handling a lot of data.	Businesses or advanced projects.
unstructured	Reads different types of files like PDFs, Word docs, etc.	Can handle many file formats and unstructured data.	Working with various file types.
local	Runs everything on your own machine without sending data out.	Keeps your data private.	Those concerned about data privacy.
unstructuredtosql	Takes data from different files and puts it in a SQL database. Then it uses SQL to answer questions.	Great for complex queries.	Advanced data manipulation and queries.

Simple Way to Run the Examples on LLM App

Considering you've done the steps before, here's a recommended, step-by-step process to run the examples easily:

Considering you've done the steps before, here's a recommended, step-by-step process to run the examples easily:

1 - Open a terminal and navigate to the LLM App repository folder:

cd llm-app

2 - Choose Your Example. The examples are located in the examples folder. Say you want to run the 'alert' example. You have two options here:

• Option 1: Run the centralized example runner. This allows you to quickly switch between different examples:

  python run_examples.py alert

---

• Option 2: Navigate to the specific pipeline folder and run the example directly. This option is more focused and best if you know exactly which example you're interested in:

  python examples/pipelines/contextful/app.py

That's it! 😄

By following these steps, you're not just running code; you're actively engaging with the LLM App’s rich feature set, which can include anything from real-time data syncing to triggering alerts on critical changes in your document store.

It's a step closer to implementing your LLM application that can have a meaningful impact.