Project: Retrieval-Augmented Generation

(RAG)

1. Name of Project

RAGLite

2. Business Details about Project

This project demonstrates a Retrieval-Augmented Generation (RAG) project focused on

simplicity and lightweight implementation. It aims to make retrieval-augmented pipelines

more accessible and efficient for various business needs.

3. Technical Details about Project

[Describe the architecture of the RAG solution, including how retrieval and generation

components interact. Include data flow, main algorithms/models used, and relevant

technical considerations such as scalability, security, and data privacy.]

RAGLite integrates retrieval and generation components, using Python and libraries

such as LangChain, FAISS, and Ollama API.

The architecture involves:

● Data is split into chunks for efficient processing

● Embeddings are computed for each chunk using models served via an Ollama

API

● Dot product similarity is used for fast retrieval of relevant content in response to

queries

● The generation step utilizes retrieved results to construct context-aware

responses

Technical considerations include scalability, modularity, support for different

embedding/generation models, and the ability to handle various data formats securely.

4. Implementation (in 5 lines)

● Step 1 - Initial setup/data ingestion: Import key libraries, set up Ollama API

and define chunking function

● Step 2 - Retrieval process: Split sample text into chunks and compute embeddings

via Ollama API

● Step 3 - Generation step: Select top relevant chunks using similarity measures and

prepare them as context

● Step 4 - Integration/User Interface: Use Python functions to combine retrieval and

generation, enabling a seamless user query-to-response system

● Step 5 – Deployment/Monitoring: The code is lightweight and can be easily

integrated into various Python-based environments for testing and monitoring

5. Sample Input and Output

● Sample Input: Query such as “Why Python is important for Generative AI?”

● Sample Output: Generated answer outlines Python’s role in Generative AI (ease of

use, libraries, community, etc.), supported by retrieved knowledge chunks.

6. Tech Stack

● Main Technologies/Frameworks: Python, LangChain, FAISS, Ollama

● These enable modular retrieval, embedding, and generation functionalities with

high flexibility and scalability

7. One Best Benefit for Business

The most significant business advantage offered by RAGLite is improving decision-making

speed by providing users with context-aware, accurate and relevant AI-generated

responses using up-to-date internal or external knowledge, leading to better productivity

and operational efficiency.