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Fred Data Analysis

Fred Data Analysis is a TypeScript full-stack application and analysis project for retrieving U.S. economic data from the FRED API, storing processed data in MongoDB, indexing it with Pinecone, and exploring it through regression analysis, plots and a retrieval-augmented chatbot. Its frontend uses React and Material UI, while its backend uses Node.js and Express.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Fred Data Analysis — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to explore a FRED-based economic-data workflow or review a mockup that combines regression analysis, vector retrieval and chatbot querying.

Who it is for

  • People exploring U.S. economic indicators and trends using FRED data.
  • Developers and learners examining a full-stack example that combines data ingestion, regression analysis, vector search and chatbot querying.

Documented uses

  • Retrieve and preprocess economic data from the FRED API and store it in MongoDB.
  • Examine economic trends with linear, polynomial, daily-percent-change and logarithmic regression, with plots and summaries.
  • Index economic data in Pinecone for vector search and retrieval-augmented chatbot questions.
Before you choose

Requirements & limits

The README provides a Vercel frontend deployment link and a backend link that opens Swagger UI. It also documents local setup requirements. The repository lists an MIT license.

  • The project is explicitly described as a mockup full-stack application.
  • The documented data path uses FRED, MongoDB and Pinecone; local setup requires API credentials and database configuration.
  • Google AI is listed among the prerequisites; OpenAI, Claude and Azure OpenAI credentials are optional for chatbot functionality.
  • The analysis report covers four regression approaches and includes plots and AI-generated, data-backed summaries.
  • Changing to another structured economic-data API is marked Likely compatible and would require adapting retrieval and schema mapping.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the local project configuration

Set up the documented prerequisites and backend environment file before following the repository’s application workflow.

Before you start

  • Node.js v18 or later
  • MongoDB and a MongoDB URI
  • Pinecone account, API key and index name
  • Google AI account and API key
  • FRED API key

Steps

  1. Clone the repository and change into its project directory.

  2. Create a .env file in the backend directory.

  3. Add the documented FRED, Google AI, Pinecone and MongoDB settings to the file.

  4. Add optional chatbot-provider credentials and Azure OpenAI settings if using those providers.

  5. Install dependencies with npm install.

Expected outcome

The documented local prerequisites and backend configuration are in place for proceeding to the project’s application instructions.

Guide limitations

  • This covers setup only; it does not establish that the application has been run successfully.
  • The README describes later data analysis and ingestion, vectorization, and application-running stages separately.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Fred Data AnalysisRetrieve and preprocess economic data from the FRED API and store it in MongoDB.Cloud, WebThe README provides a Vercel frontend deployment link and a backend link that opens Swagger UI. It also documents local setup requirements. The repository lists an MIT license.
AttuBrowse databases, collections and records; edit data inline; import or export CSV, JSON and Parquet.Docker, Self-hostedAttu is documented as a self-hosted web application or desktop application. The v3.0 documentation lists Docker, Kubernetes and a standalone server package for web use, plus desktop releases for macOS, Linux and Windows.
Text-to-SQL CopilotAsk questions about database contents in natural language.CLIThe repository is public and MIT-licensed. The README describes Hugging Face Inference API access as free, and requires a Hugging Face API token for the documented model connection.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
hoangsonww/FRED-Data-Analysis · Official homepage title · retained in its source language
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