relari-ai / examples

Examples for continuous-eval

continuous-eval examples

This repo contains end-to-end examples of GenAI/LLM applications and evaluation pipelines set up using continuous-eval.

Checkout continuous-eval repo and documentation for more information.

Example Name	App Framework	Eval Framework	Description
Simple RAG	Langchain	continuous-eval	Simple QA chatbot over select Paul Graham essays
Complex RAG	Langchain	continuous-eval	Complex QA chatbot over select Paul Graham essays
Simple Tools	LlamaIndex	continuous-eval	Math question solver using simple tools
Context Augmentation Agent	LlamaIndex	continuous-eval	QA over Uber financial dataset using agents
Sentiment Classification	LlamaIndex	continuous-eval	Single label classification of sentence sentiment

Installation

git clone https://github.com/relari-ai/examples.git && cd examples
poetry install

Add LLM API keys in .env (reference .env.example) for select applications.

• COHERE API_KEY for Cohere Rerankers in RAG examples
• GOOGLE_API_KEY for all LLM calls

Get started

In each application folder (examples/[langchain|llamaindex]/APP_NAME/), here are the files:

• pipeline.py defines the application pipeline and the evaluation metrics / tests.
• app.py contains the LLM application. Run the application to get the outputs (saved as results.jsonl)
• eval.py runs the metrics / tests defined by pipeline.py (saved as metrics_results.json and test_results.json)

Depending on the application, the source data for the application (documents and embeddings in Chroma vectorstore) and evaluation (golden dataset) is also provided. Note that for the evaluation golden dataset, there are always two files:

• dataset.jsonl contains the inputs (questions) and reference module outputs (ground truths)
• manifest.yaml defines the structure of the dataset for the evaluators.

Tweak metrics and tests in pipeline.py to try out different metrics.