run2-enterprise-product-data-analysis

name	run2_enterprise-product-data-analysis
description	Advanced techniques for analyzing complex enterprise product JSON files containing messages, documents, and transcripts.

Enterprise Product Data Analysis

Enterprise product data often combines unstructured communication (Slack messages) with structured documents and meeting transcripts.

1. Document-Centric Retrieval

Documents are usually stored in a documents array. Each entry has metadata like author, type, feedback, and document_link. To find reviewers, trace the feedback comments back to the Message objects in the same file.

2. Transcript and Message Correlation

When a task asks for "key reviewers", correlate the feedback field in documents with the User IDs who made similar suggestions in the Message history.

3. Handling Large Files Surgicaly

For files over 500KB, use grep_search to find line numbers and read_file with narrow ranges (±10 lines) to understand the context of mentions.

4. Entity Mapping

Consolidate mappings between userId and names early by sampling employee.json. Note that some IDs might not be present in all metadata files, requiring a search across the entire /root/DATA directory.