enterprise-data-retrieval

name	enterprise-data-retrieval
description	Techniques for retrieving and querying structured enterprise data from JSON files containing slack messages, documents, meeting transcripts, and URLs.

Enterprise Data Retrieval

Data Structure

Enterprise data is typically organized by product in /root/DATA/products/<ProductName>.json with the following top-level keys:

• slack: list of slack messages with Channel, Message, ThreadReplies, id
• documents: list of docs with content, date, document_link, author, type, id
• meeting_transcripts: list of transcripts with transcript, date, document_type, participants, id
• meeting_chats: list of chat messages
• urls: list of URLs with link, description, id
• prs: list of PRs with title, summary, link, mergeable, merged, number, state, user, created_at, reviews, id

Finding Documents by Type

import json

with open('/root/DATA/products/ContentForce.json') as f:
    data = json.load(f)

# Find documents by type (e.g., "Market Research Report")
docs = [d for d in data['documents'] if 'market research' in d['type'].lower() or 'market research' in d['content'].lower()]

Finding Authors and Reviewers

# Documents have an 'author' field; PRs have 'user' and 'reviews'
for doc in docs:
    print(doc['author'])  # author employee ID or name

# For PRs
for pr in data['prs']:
    print(pr['user'])  # PR author
    for review in pr['reviews']:
        print(review)  # reviewer info

Searching Slack for Keywords

keyword = 'competitor'
results = []
for msg in data['slack']:
    if keyword.lower() in msg['Message'].lower():
        results.append(msg)
    for reply in msg.get('ThreadReplies', []):
        if keyword.lower() in reply.lower():
            results.append({'parent': msg, 'reply': reply})

Metadata Files

• /root/DATA/metadata/employee.json: employee records mapping IDs to names
• /root/DATA/metadata/customers_data.json: customer records
• /root/DATA/metadata/salesforce_team.json: sales team info