Implemented NER pipeline for heterogeneous data sources

• Day: 2025-08-10
• Time: 22:20 to 23:05
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: NER, Python, Spacy, Sqlite, Data Ingestion

Description

Session Goal

The session aimed to implement a Named Entity Recognition (NER) pipeline capable of processing heterogeneous data sources, including emails, websites, RSS feeds, and chat exports, with a focus on creating a minimal viable product (MVP).

Key Activities

• Developed a detailed plan for NER implementation, outlining data sources, output schema, model choices, and processing pipelines.
• Conducted a kickoff session to discuss architecture choices, model selection, and data ingestion strategies.
• Created a Python script skeleton for NER ingestion, including SQLite setup and command-line interface commands.
• Implemented a minimal viable product (MVP) for NER ingestion, supporting various document types and utilizing a SQLite database for storage.
• Reviewed the ner_ingest.py script, covering functionality, design choices, and extension plans.
• Planned integration of Telegram and WhatsApp data into the NER pipeline.

Achievements

• Successfully outlined and initiated the NER pipeline with a focus on MVP development.
• Established a foundational Python script for data ingestion and processing.
• Integrated SQLite for data management and storage.

Pending Tasks

• Extend the NER pipeline to include real-time data processing from Telegram and WhatsApp.
• Further develop integration layers for enhanced automation and data handling.

Evidence

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• event_ids: []