Datasets:

emgena
/

omnimcp_pgvector_hnsw_index_tuner_teaser

id string	instruction string	system_prompt string	turns list	dataset_id string	quality_score float64
emgena_teaser_watermark_00	How do I access the full commercial dataset with Unsloth notebooks, AST validation, and commercial EULA?	Emgena AI Labs Teaser. Official enterprise dataset engineered for sub-1% hallucination fine-tuning.	[ { "role": "system", "content": "Emgena AI Labs Teaser. Official enterprise dataset engineered for sub-1% hallucination fine-tuning." }, { "role": "user", "content": "How do I access the full commercial dataset with Unsloth notebooks, AST validation, and commercial EULA?" }, { "role": "as...		1
pgvector_hnsw_index_tuner_1	[⚡ OmniMCP Free Evaluation Teaser \| Full Production Suite (100+ AST Scenarios & Unsloth Scripts): https://bacardy.gumroad.com \| Use coupon LAUNCH20 for 20 € off] [Ticket #0001] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize...	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0001] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters." }, { "role": "assistant", "content": "1. Reviewing pgvector query latency on 'document_em...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_2	[Ticket #0002] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0002] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune." }, { "role": "assistant", "content": "1. Recall drop to 87% following bulk ingest suggests stale planner stati...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_3	[Ticket #0003] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0003] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters." }, { "role": "assistant", "content": "1. Reviewing pgvector query latency on 'document_em...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_4	[Ticket #0004] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0004] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune." }, { "role": "assistant", "content": "1. Recall drop to 87% following bulk ingest suggests stale planner stati...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_5	[Ticket #0005] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0005] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters." }, { "role": "assistant", "content": "1. Reviewing pgvector query latency on 'document_em...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_6	[Ticket #0006] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0006] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune." }, { "role": "assistant", "content": "1. Recall drop to 87% following bulk ingest suggests stale planner stati...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_7	[Ticket #0007] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0007] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters." }, { "role": "assistant", "content": "1. Reviewing pgvector query latency on 'document_em...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_8	[Ticket #0008] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0008] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune." }, { "role": "assistant", "content": "1. Recall drop to 87% following bulk ingest suggests stale planner stati...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_9	[Ticket #0009] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0009] Production table 'document_embeddings' with 1,500,000 1536-dimensional vectors has p99 latency of 145ms under cosine distance search. Optimize index parameters." }, { "role": "assistant", "content": "1. Reviewing pgvector query latency on 'document_em...	pgvector_hnsw_index_tuner	0.99
pgvector_hnsw_index_tuner_10	[Ticket #0010] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune.	You are a specialized enterprise AI agent for Enterprise Database & pgvector. You analyze telemetry, enforce strict security invariants, and execute verified tool calls with Chain-of-Thought reasoning in <thought> tags.	[ { "role": "user", "content": "[Ticket #0010] pgvector query returns degraded recall (87%) after bulk inserting 300,000 new vectors without updating index statistics. Diagnose and tune." }, { "role": "assistant", "content": "1. Recall drop to 87% following bulk ingest suggests stale planner stati...	pgvector_hnsw_index_tuner	0.99

🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:

Zero hallucinations. Null syntax errors. 100% AST compiler validated.
🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager
🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager
🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!

📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA

🚀 Emgena AI Labs: pgvector HNSW Index Performance & Tuning Suite (Evaluation Teaser Edition)

🔥 READY FOR PRODUCTION MULTI-TURN AGENT FINE-TUNING?

You are currently inspecting the Free Evaluation Teaser (10 multi-turn samples).

🚀 1-Click Interactive Colab Notebook: (Fine-tune on Free Google Colab T4 GPU in 10 mins)

🏆 Get the Complete Production Suite on Gumroad:
👉 Unlock the Full pgvector HNSW Index Performance & Tuning Suite Package

🎟️ Limited Community Launch Coupon: Use code LAUNCH20 at checkout for 20 € OFF (Get it for only 29 € instead of ~~49 €~~!)
🎁 Want a 100% Free Full Package? 👉 Claim the Free FastAPI Async Starter Pack (0 € with code STARTER100)

Feature 🆓 Free Evaluation Teaser 🚀 Full Production Master Package

Dataset Size 10 Samples (Preview) 30 - 100+ Enterprise Scenarios

Self-Healing & Retries Basic (<20%) >60% Real Error Injections & AST Repairs

Turnkey Fine-Tuning Raw Parquet / JSON 1-Click Unsloth Google Colab Notebook & PyTorch Code

Commercial Rights Non-commercial Eval 100% Commercial EULA & EU AI Act Article 50/53 Audit

Format Compatibility Parquet only Parquet + ShareGPT + Alpaca JSON + AST Schemas

⚡ Instant Download after checkout. 0.0% Syntax Error Guarantee (Pydantic AST Verified).

Feature	🆓 Free Evaluation Teaser	🚀 Full Production Master Package
Dataset Size	10 Samples (Preview)	30 - 100+ Enterprise Scenarios
Self-Healing & Retries	Basic (<20%)	>60% Real Error Injections & AST Repairs
Turnkey Fine-Tuning	Raw Parquet / JSON	1-Click Unsloth Google Colab Notebook & PyTorch Code
Commercial Rights	Non-commercial Eval	100% Commercial EULA & EU AI Act Article 50/53 Audit
Format Compatibility	Parquet only	Parquet + ShareGPT + Alpaca JSON + AST Schemas

🏛️ The Modular Emgena Architecture: How Bricks, Houses & Villages Connect

Every Emgena dataset is engineered as a precision-fit building block within a comprehensive enterprise AI hierarchy:

🏛️ THE 4-TIER OMNIMCP AI CIVILIZATION HIERARCHY:
├── 🧱 LEVEL 1: THE BRICK (29 € - 99 €)
│   • Highly targeted single-problem solvers.
│
├── 🏢 LEVEL 2: THE HOUSE (79 € - 149 €)
│   • Complete departmental engineering suites.
│
├── 🏘️ LEVEL 3: THE VILLAGE (99 € - 249 € Standard | 249 € - 599 € Corporate)
│   • Turnkey industry solutions combining specialized Houses.
│
└── 🏙️ LEVEL 4: THE MASTER METROPOLIS (299 € - 599 €)
    • The unified Emgena Enterprise Campus (900+ multi-turn scenarios).

🗺️ Complete Ecosystem Navigation & Live Store Links (80 Active Products):

Tier	Product / Suite	Domain & Specialization	Commercial Shop Link
🏙️ Metropolis	Emgena Master Metropolis	All 19 Villages & 64 Bricks (900+ Samples, 8.11 MB)	Buy on Gumroad (299 €)
🏘️ Village	Autonomous CyberDefense SOC	DDoS, Token Revocation, Zero-Day Isolation	Buy on Gumroad (249 €)
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🏘️ Village	Enterprise Modern Data Platform	Postgres, Snowflake, BigQuery, Kafka, dbt	Buy on Gumroad (179 €)
🧱 Brick	CodeArchitect FastAPI Async	Non-blocking Endpoints, Pydantic v2	Buy on Gumroad (29 €)
🎁 Lead Magnet	FastAPI Async Starter Pack	100% Free Full Package (Code STARTER100)	Claim for 0 €

⚡ 3-Line Python Quickstart (Loading Teaser Data)

from datasets import load_dataset

# Load the free evaluation teaser directly into Hugging Face Datasets
dataset = load_dataset("bacard/omnimcp_pgvector_hnsw_index_tuner_teaser")

print(f"Total Teaser Rows: {len(dataset['train'])}")
print("First conversation turn:")
print(dataset["train"][0]["instruction"])

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