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Data Source Integration Guide for ChainAlign

1. Overview​

This document outlines the process for integrating new external data sources into the ChainAlign platform, specifically for enriching the "Live Market and Supply Chain Intelligence Dashboard." It covers the steps from identifying a new data source to its display on the frontend, including data acquisition, transformation, AI-driven curation, and frontend integration.

2. Process for Adding a New Data Source​

2.1. Data Source Identification​

  • Purpose: Clearly define the business need and the type of intelligence the new data source will provide.
  • Source Type: Determine if the source is an API (REST, GraphQL, SDMX), a database, a file feed (CSV, XML, JSON), or a web scrape.
  • Access & Authentication: Understand the authentication mechanisms (API keys, OAuth, tokens) and any rate limits or usage policies.

2.2. Data Acquisition​

  • Backend Service: Create a new service file (e.g., backend/src/services/newDataSourceService.js) to handle API calls or data retrieval logic.
  • Libraries: Utilize appropriate libraries (e.g., axios for HTTP requests, database drivers).
  • Error Handling: Implement robust error handling, retry mechanisms, and logging for acquisition failures.

2.3. Data Transformation and Standardization​

  • Normalization: Convert raw data into a standardized format consistent with ChainAlign's internal data models.
  • Schema Mapping: Define how fields from the external source map to ChainAlign's data schema.
  • Data Cleaning: Handle missing values, data type conversions, and remove duplicates.

2.4. Integration with AIManager (for News Enrichment)​

If the new data source is intended to enrich news articles (similar to World Bank data):

  • Import Service: Import the new data source service into backend/src/services/AIManager.js.
  • Data Fetching: Within AIManager.enrichNewsArticle, call the new service to fetch relevant data based on article content (e.g., country, commodity).
  • Context Formatting: Format the fetched data into a concise context string (e.g., newDataSourceContext) to be injected into the prompt for Google Gemini.
  • Prompt Augmentation: Add the newDataSourceContext to the constructAugmentedPrompt function in AIManager.js.

2.5. Storage Considerations​

  • Operational Store (PostgreSQL): For highly structured, relational data that requires complex querying and transactions.
  • Semi-structured Store (Cloud Firestore): For AI-processed data, metadata, or less structured information (e.g., raw news articles, enriched article insights).
  • Vector Database: For embeddings and similarity search (e.g., vectorDbService.js).

2.6. Error Handling and Logging​

  • Centralized Logging: Ensure all data acquisition and processing steps log relevant information and errors to a centralized logging system (e.g., Google Cloud Logging).
  • Alerting: Set up alerts for critical failures in data pipelines.

3. Data Curation​

3.1. AI-driven Curation (Google Gemini)​

  • Prompt Engineering: Craft effective prompts in AIManager.js to guide Gemini in extracting specific insights, assessing risks, and summarizing impacts from the raw data combined with other contexts.
  • Output Structure: Define the expected JSON output structure from Gemini to ensure consistency.

3.2. Human-in-the-Loop (Optional)​

  • Review Queue: For critical or uncertain data points, implement a mechanism for human review and correction.
  • Feedback Loop: Use human feedback to fine-tune AI models or prompt engineering.

4. Frontend Integration​

4.1. Data Consumption​

  • API Endpoints: Ensure the backend exposes an API endpoint (e.g., /api/news) that returns the enriched data, including the new data source's insights.
  • Frontend Fetching: Update frontend components (e.g., IntelligenceFeedPage.jsx) to fetch data from the relevant backend API.

4.2. Component Updates​

  • Display Logic: Modify existing components (e.g., article cards) or create new ones to visually represent the new data.
  • UI/UX Considerations: Design intuitive ways to display the new intelligence, considering readability, interactivity, and overall dashboard flow.
  • Filtering/Sorting: Add new filters or sorting options if the new data introduces relevant dimensions.

5. Testing and Validation​

5.1. Unit Tests​

  • Write unit tests for the new data acquisition service and any transformation logic.

5.2. Integration Tests​

  • Develop integration tests to verify the end-to-end flow, from data acquisition to AI enrichment and API response.

5.3. Monitoring​

  • Implement monitoring for data freshness, data quality, and API performance of the new data source.