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[GraphRAG] Develop Data Ingestion Module with Schema for Flare Mainnet Transactions into Neo4j #15

@dineshpinto

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@dineshpinto
  • Description:
    Create a robust data ingestion module for the GraphRAG engine. This module builds on the ecosystem Goldsky integration
    and normalizes the raw transaction data, and transforming it into a graph-friendly format suitable for storage in the Neo4j database.
  • Acceptance Criteria:
    • The module can successfully connect to the Flare Mainnet to receive a stream of transaction data or batch updates.
    • Incoming data is correctly parsed, normalized to a standard internal representation, and transformed into graph structures (nodes and edges).
    • The transformed graph data is ingested into the Neo4j database, adhering to the pre-defined graph schema.
    • The ingestion process is designed to be robust, capable of handling potential data inconsistencies, retries, and network issues.
    • A detailed graph schema document is produced, clearly defining node labels (e.g., Transaction, Account, Contract, Token), edge types (e.g., SENT_TO, INTERACTED_WITH, DEPLOYED_CONTRACT, TRANSFERRED_TOKEN), and the properties associated with each.
    • The schema is designed to effectively capture the relationships and data points necessary for answering complex contextual queries about blockchain activity.
    • The designed schema is implemented in the Neo4j setup.
  • Key Files/Modules Involved (Tentative):
    • flare_ai_kit/rag/graph/schema.py (New file to be created)
    • flare_ai_kit/rag/graph/transformer.py (New file to be created)
  • Tasks / Implementation Steps:

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