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| Aspect | Details |
|--------|----------|
| Type | Autonomous, cloud‑hosted AI assistant (public‑access tier) |
| Core Engine | Fine‑tuned LLM based on the “E57” family (≈ 175 B parameters) with a focus on natural‑language understanding, structured data extraction, and procedural reasoning. |
| Primary Use‑Cases | • Customer‑support bots
• Real‑time data‑driven decision support
• Incident‑response triage
• Knowledge‑base querying and enrichment |
| Access Model | Public API (REST + WebSocket) with optional on‑prem “edge‑node” for low‑latency, privacy‑sensitive workloads. |
| Key Differentiators | • Built‑in compliance layers (GDPR, CCPA, HIPAA‑lite)
• “Self‑healing” prompt‑management that auto‑re‑weights relevance scores
• Multi‑modal support (text + structured JSON + light image captions). |
response = client.chat(
messages=[
"role": "system", "content": "You are Lenka, a helpful public agent.",
"role": "user", "content": "What’s the weather in Oslo today?"
],
temperature=0.2,
max_tokens=64
)
print(response.choices[0].message["content"])
Result – a concise, JSON‑ready answer (e.g.,
"temp_c": 7, "condition":"Cloudy"). public agent e57 lenka
If all ✔️, you’re ready to ship Lenka into production! | Aspect | Details | |--------|----------| | Type
| Aspect | Details |
|--------|----------|
| Type | Autonomous, cloud‑hosted AI assistant (public‑access tier) |
| Core Engine | Fine‑tuned LLM based on the “E57” family (≈ 175 B parameters) with a focus on natural‑language understanding, structured data extraction, and procedural reasoning. |
| Primary Use‑Cases | • Customer‑support bots
• Real‑time data‑driven decision support
• Incident‑response triage
• Knowledge‑base querying and enrichment |
| Access Model | Public API (REST + WebSocket) with optional on‑prem “edge‑node” for low‑latency, privacy‑sensitive workloads. |
| Key Differentiators | • Built‑in compliance layers (GDPR, CCPA, HIPAA‑lite)
• “Self‑healing” prompt‑management that auto‑re‑weights relevance scores
• Multi‑modal support (text + structured JSON + light image captions). |
response = client.chat(
messages=[
"role": "system", "content": "You are Lenka, a helpful public agent.",
"role": "user", "content": "What’s the weather in Oslo today?"
],
temperature=0.2,
max_tokens=64
)
print(response.choices[0].message["content"])
Result – a concise, JSON‑ready answer (e.g.,
"temp_c": 7, "condition":"Cloudy").
If all ✔️, you’re ready to ship Lenka into production!