These capabilities are currently in beta. Please contact founders@tryneum.com with any questions or asks.

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Overview

Configuring RAG pipelines requires iteration across different parameters ranging from pre-processing loaders and chunkers, to the actual embedding model being used. To assist in testing different configurations, Neum AI provides several tools to test, evaluate and compare pipelines.

To get started with evaluation, first make sure you have a pipeline configured.

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Datasets

Datasets provide the ability to create a list of test to run against a pipeline. Datasets are made up of DatasetEntry objects which each represent a test. Each DatasetEntry objects contains a query, an expected output and an id.

DatasetEntry(
	id='1', 
	query="What is Retrieval Augmented Generation (RAG)?", 
	expected_output="The blog explains RAG as a method that helps in finding data quickly by performing searches in a 'natural way' and using that information to power more accurate AI applications"
)

Datasets can be configured to run an evaluation. Evaluations supported include:

  • Cosine Evaluation: Compares the vector embeddings between the retrieved chunk and the expected output.
  • LLM Evaluation: Uses an LLM to check the quality and correctness of the retrieved information in answering the query at hand. (Requires you to set an OpenAI key as an enviornment variable: OPENAI_API_KEY)

To create a dataset:

!pip install neumai-tools

from neumai_tools.DatasetEvaluation.Dataset import Dataset
from neumai_tools.DatasetEvaluation.DatasetUtils import DatasetEntry
from neumai_tools.DatasetEvaluation.Evaluation import CosineEvaluation

dataset = Dataset(name="Test 1", dataset_entries=[
    DatasetEntry(id='1', query="What is Retrieval Augmented Generation (RAG)?", expected_output="The blog explains RAG as a method that helps in finding data quickly by performing searches in a 'natural way' and using that information to power more accurate AI applications"),
    DatasetEntry(id='2', query="How does the RAG system function?", expected_output="It describes the process where data is extracted, processed, embedded, and stored in a vector database for fast semantic search lookup. This data is then used by AI applications for providing accurate responses based on user inputs"),
    DatasetEntry(id='3', query="What are the challenges in scaling RAG?", expected_output=" The blog discusses the challenges in ingesting and synchronizing large-scale text embeddings for RAG, including understanding the volume of data, ingestion time, search latency, cost, and the complexities of data embedding"),
    DatasetEntry(id='4', query="What specific technologies or programming languages are used in the development of Neum AI's RAG system?", expected_output="Neum AI is written in Python.")
], evaluation_type=CosineEvaluation)

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Run a test

Once a dataset is created, we can run it against a pipeline. We also support the ability to run a dataset against a pipeline collection to compare the results from multiple pipelines at the same time.

results = dataset.run_with_pipeline(pipeline=pipeline) # pipeline represents the configured pipeline you have created

print(f'Dataset Result ID: {results.dataset_results_id}')
for result in results.dataset_results:
    print(f"For query: {result.dataset_entry.query} \n Expected Outcome: {result.dataset_entry.expected_output} \n Actual Result: {result.raw_result.metadata['text']} \n Score: {result.score}")

The result will include a score or an evaluation matrix depending on the type of evaluation being used.

Output:

Dataset Result ID: 1907ecf0-2ef2-47c6-a287-b08dd743c36c

For query: What is Retrieval Augmented Generation (RAG)?
Expected Outcome: The blog explains RAG as a method that helps in finding data quickly by performing searches in a 'natural way' and using that information to power more accurate AI applications
Actual Result: As we’ve shared in other blogs in the past, getting a Retrieval Augmented Generation (RAG) application started is pretty straightforward. The problem comes when trying to scale it and making it production-ready. In this blog we will go into some technical and architectural details of how we do this at Neum AI, specifically on how we did this for a pipeline syncing 1 billion vectors.First off, can you explain what RAG is to a 5 year old? - Thanks ChatGPT
Score: 0.8666629542920514

For query: How does the RAG system function?
Expected Outcome: It describes the process where data is extracted, processed, embedded, and stored in a vector database for fast semantic search lookup. This data is then used by AI applications for providing accurate responses based on user inputs
Actual Result: RAG helps finding data quickly by performing search in a “natural way” and use that information/knowledge to power a more accurate AI application that needs such information!This is what a typical RAG system looks likeData is extracted, processed, embedded and stored in a vector database for fast semantic search lookup
Score: 0.8942534447233399

For query: What are the challenges in scaling RAG?
Expected Outcome:  The blog discusses the challenges in ingesting and synchronizing large-scale text embeddings for RAG, including understanding the volume of data, ingestion time, search latency, cost, and the complexities of data embedding
Actual Result: As we’ve shared in other blogs in the past, getting a Retrieval Augmented Generation (RAG) application started is pretty straightforward. The problem comes when trying to scale it and making it production-ready. In this blog we will go into some technical and architectural details of how we do this at Neum AI, specifically on how we did this for a pipeline syncing 1 billion vectors.First off, can you explain what RAG is to a 5 year old? - Thanks ChatGPT
Score: 0.8438287364009266

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