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README

This directory contains the necessary files to deploy a basic RAG pipeline using the R2R framework with an Ionic Commerce Integration providing product recommendations and links based on query

To test this application locally, run the following

# Install
pip install -r requirements.txt

export OPENAI_API_KEY=<your_key>
export LOCAL_DB_PATH=local.sqlite

# Launch
uvicorn src.app:app

# Example Client
python -m src.client

Visit SciPhi for more information on how to customize your RAG application. Visit Ionic and their docs for more information about embedding ecommerce.

File Structure

The main files in this directory are:

  • src/app.py: The main entry point for the application.
  • requirements.txt: The file specifying the Python dependencies required by the application.

Application Overview

The application is built using the r2r library and follows a pipeline-based architecture. The main entry point is in the src/app.py file, which creates a pipeline using the E2EPipelineFactory.create_pipeline() method. This method reads the configuration settings from the config.json file to set up the various components of the pipeline.

Configuration

The config.json file is used to control the deployment settings of the application. You can modify the configuration options in this file to customize the behavior of the pipeline components, such as the database provider, continuous evaluation settings, embedding settings, text splitter settings, and language model provider.

This file contains the following configuration options:

  • database: Specifies the vector database provider. In this case, the default is set to "sciphi". This results in a SciPhi managed Lantern database. Selecting an alternative provider like qdrant allows the developer to connect with their remote cloud offering.
  • evals: Specifies the evaluation provider (set to "deepeval") and the pipeline evaluation frequency.
  • embedding: Specifies the embedding provider (set to "openai"), the embedding model, dimension, and batch size.
  • text_splitter: Specifies the text splitting settings, including the chunk size and chunk overlap.
  • language_model: Specifies the language model provider. In this case, it is set to "litellm".