othersyntheticspanishmexican-spanishcustomer-supportconversationsjsonlqaimport-testingpython

Synthetic Spanish support QA: 8,000 template conversations, 22 fields, JSONL

Fully synthetic fixture; fictional dates January 1-April 1, 2031. Not real business observations. · Updated Nov 10, 2026

Fixed price

Sample structure: Not assessed

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Seller: Alan

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: CC BY 4.0. Commercial use, modification and redistribution permitted with attribution, license link and indication of changes. No warranty, future updates or support SLA.
Source / creator: Alan - AI-assisted synthetic template generation
Collection method: AI-assisted Python standard-library template generator; seed42; fixed fictional date anchor2031-04-01. No external source records/customer logs. Reserved contact placeholders.
Coverage / snapshot: Fully synthetic fixture; fictional dates January 1-April 1, 2031. Not real business observations.
Data last updated: Not documented
Update schedule: Fixed snapshot v1.1. No future updates included or promised.

License terms ↗

Structural QA fixture only, not validated model training data or benchmark. Repeated phrases; generated labels may contradict messages. No statistical or regional-authenticity claims. No support SLA. CC BY4.0 permits redistribution.

Sample structure score: Not assessed

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About this data

Load 8,000 artificial Spanish support conversations to test JSONL imports, ticket-routing demos and conversation UI layouts. AI-assisted templates using some Mexican Spanish expressions; not real support logs or a validated training set. Each record has 22 top-level fields and 5-13 alternating turns. Six topic labels and five simulated channel labels. ZIP includes JSONL, deterministic Python standard-library generator (seed 42), structural validator, 12 corruption tests and README. Clean ZIP passes tests; rebuilding reproduces the same JSONL bytes. Suggested checks: validate field presence, IDs, turn order, timestamps and reserved contact markers; render long/short conversations; test filters across topic/channel labels. Dates are fictional January 1-April 1, 2031. Contact placeholders use example.invalid emails, non-dialable TEST-PHONE labels, and SYNTHETIC person/company prefixes. Limits: small phrase pools repeat. Sentiment, resolution, satisfaction and secondary labels are generated and may contradict the text; no human semantic review. Not a regional-language benchmark, representative business sample or proof of model quality. No external customer records or accounts were used. CC BY 4.0: reuse and redistribution allowed with attribution; see README for full terms and limits. No support SLA, future updates or application compatibility promised. Automatic public samples remain disabled; full files are paid.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example

python3 retrieve-dataset.py b051f6f3-d0a8-4c8d-afe6-5c0362d20f07 --output dataset.bin

Full supplier documentation

AI-assisted synthetic template fixture for JSONL import checks, ticket-routing demos and UI load tests. Exactly 8,000 conversations, 22 top-level fields, 5-13 alternating turns, 6 topic labels and 5 simulated channels. ZIP includes JSONL, deterministic Python standard-library generator (seed 42, fictional dates January 1-April 1, 2031), structural validator, 12 corruption tests and README. Reserved example.invalid emails, non-dialable TEST-PHONE labels and SYNTHETIC person/company prefixes. No real customer logs or external accounts used. Not a validated training set, benchmark or representative business data. Small phrase pools repeat. Generated sentiment, resolution, satisfaction and secondary intent labels are not human annotations and may disagree with text. Monetary metadata may differ from amounts mentioned. Replies may be unrealistic or contradictory. No regional-authenticity, production compatibility, privacy-audit or statistical-fidelity certification. Not legal, tax or refund advice. Structural checks verify IDs, turn order, timing, reserved contacts and metadata consistency; 12 tests pass. CC BY 4.0: commercial use, modification and redistribution permitted with attribution, license link and change notice. Paid convenience package, not exclusive data. No future updates, support SLA or warranty. Automatic sample generation disabled; no full-file public preview.

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For AI Agents

Via MCP Server

# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "Synthetic Spanish support QA: " })
// Found: b051f6f3-d0a8-4c8d-afe6-5c0362d20f07
buy_now({ dataset_id: "b051f6f3-d0a8-4c8d-afe6-5c0362d20f07" })  // uses your saved card

Via REST API

# Authenticate with your API key:
curl https://api.databazaar.io/datasets/b051f6f3-d0a8-4c8d-afe6-5c0362d20f07 \
  -H "Authorization: Bearer YOUR_API_KEY"

# Purchase:
curl -X POST https://api.databazaar.io/datasets/b051f6f3-d0a8-4c8d-afe6-5c0362d20f07/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "b051f6f3-d0a8-4c8d-afe6-5c0362d20f07"}'