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Building a Statistical Research GPT with DeepLake and Eurostat

In this article, we aim to construct a GPT capable of answering statistical questions using DeepLake and the Eurostat API. Additionally, we will demonstrate how to enhance the GPT's performance by leveraging Deep Memory and implementing reranking with Cohere.

Exploring Eurostat

Introduction

Open data offers tremendous value, yet locating the specific data you require can prove challenging. Eurostat, the statistical office of the European Union, provides an abundance of open, reliable data. Nonetheless, navigating through this wealth of information to find relevant data can be daunting, especially for those unfamiliar with statistical databases. Our GPT addresses this challenge by not only identifying the needed data but also presenting it in a user-friendly manner.

Why Not Just Use ChatGPT?

While ChatGPT excels at answering a wide range of questions, it struggles with queries like "How have France's CO2 emissions changed since 1990?" or "Is there a correlation between life expectancy and GDP per capita in the EU?" Such questions demand specific data that isn't readily accessible through a simple Google search.

Why Not Directly Use the Eurostat API?

Indeed, the Eurostat API is a powerful tool, and it plays a crucial role in our project. However, its lack of a comprehensive search functionality presents a significant obstacle. Without precise knowledge of the necessary dataset codes and variables, finding relevant data becomes exceedingly difficult. This is where DeepLake comes into play. DeepLake's vector store enables us to search for pertinent datasets using natural language and simple filters, bridging the gap in the Eurostat API's functionality.

Why Choose DeepLake?

Among the various vector stores available, DeepLake stands out for several reasons:

  1. Open Source: DeepLake is freely available to the public.
  2. User-Friendly: It is well-documented, straightforward to use, and supported by an active community.
  3. Deep Memory: This feature significantly enhances search performance, thereby improving our GPT's capability to locate relevant datasets. DeepLake also simplifies the development process and application in projects:
  4. Serverless Local Development: It offers a hassle-free and simple setup in form of serverless local vector store deployment.
  5. Seamless Transition to Hosted Solutions: Upgrading from a local, serverless setup to a fully hosted solution is remarkably straightforward, requiring as little as one line of code:
from deeplake import deepcopy
deepcopy(src="<path to local deeplake vector store>", dest="<path to new hosted instance>")
# That's all it takes!
  1. Supportive Developer Team: The team behind DeepLake, ActiveLoop, is exceptionally accessible and helpful. They are, without a doubt, the most reachable developer team I've ever encountered myself.

Part 1: Project Setup and Initial Steps

Setting Up the Development Environment

  • Requirements and dependencies
  • Installing DeepLake and other necessary libraries

Data Scraping and Preparation

  • Overview of the Eurostat API structure
  • Detailed walkthrough of scripts/eurostat_scraper.py

Creating and Populating the DeepLake Vector Store

  • Explanation of vector stores and their relevance
  • Step-by-step guide on using scripts/vector_store_init.py

Local API Development

  • Architecture and design choices for the Flask API
  • Detailed explanation of each function within api_search.py and data_retriever.py

Part 2: Integrating GPT with DeepLake and Eurostat

Designing the GPT Interaction Model

  • Overview of how GPT will interact with DeepLake and Eurostat
  • Structure and logic for the instruction set to GPT

Implementing GPT Instructions

  • Detailed coding guide for GPT's data retrieval instructions
  • Handling errors and exceptions in GPT requests

Testing and Validation

  • Strategies for testing GPT's ability to retrieve and interpret data
  • Validation against known datasets and queries

Part 3: Enhancing Performance with Deep Memory and Cohere

Deep Memory: Theory and Implementation

  • Conceptual overview of Deep Memory in vector search
  • Guide to training Deep Memory with Eurostat dataset titles

Integrating Cohere for Reranking

  • Introduction to reranking and its importance
  • Implementing Cohere reranking to improve GPT's data selection

Performance Evaluation

  • Metrics and methods for evaluating improvements
  • Comparative analysis: Before and after Deep Memory and Cohere integration

Part 4: Connecting DeepLake to GPT: A Template Guide

Connecting your own DeepLake Instance to a GPT

  • Templates for local Flask API and OpenAPI Spec
  • Step-by-step guide for connecting GPT to DeepLake and Eurostat