Chat Channel: Difference between revisions
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===Skills=== | ===Skills=== | ||
It is also possible to help the AI with [[Skill]] | It is also possible to help the AI with [[Skill]]s. These are "job descriptions" of how to do specific tasks and can increase the scope of of the agent's capabilities. | ||
==Attributes== | ==Attributes== | ||
Revision as of 13:24, 22 September 2026
Chat Channels define the conversation with AI Agents. Each channel can have multiple Chat Messages.
AI Agents
The Tygron Platform currently has two default AI Agent channels:
- WIKI Assistant: Searching the Tygron Wiki for information on features, configurations and guides.
- Project Coder: Can edit data in your Project, by executing TQL queries or interact with project API endpoints.
LLM
List of open source Large Language Models available to the Chat Channel Agents:
- Gemma4 (Speed): Gemma4 model[1] optimized for fast reactions.
- Qwen3.8 (Quality): Qwen3.8 model[2] optimized for quality needed for coding and TQL.
Privacy
To protect your data, the following measures are in place:
- Chat messages are stored within your Project and follow the same data and privacy terms described in your license agreement.
- All models run exclusively on Tygron-owned GPU infrastructure.
- Zero Data Retention (ZDR): no data or metadata is stored by the AI model and conversations are not used for AI training.
MCP Tools
List of available Model Context Protocol (MCP) Tools for the Chat Channel Agents:
- get-summary: Get AI Summary on a WIKI subject.
- search-page: Search the WIKI Pages.
- get-page: Get specific WIKI Page.
- validate-endpoint: Validate Tygron API Endpoint.
- validate-query: Validate Tygron TQL Query.
- execute-query: Execute TQL Query in your connected Project session.
- execute-endpoint: Execute API Endpoint in your connected Project session.
Skills
It is also possible to help the AI with Skills. These are "job descriptions" of how to do specific tasks and can increase the scope of of the agent's capabilities.
Attributes
Chat Channel attributes can be used to guide the behavior of the underlying Large Language Model (LLM).
| Attribute | Unit | Description | Default value |
|---|---|---|---|
| TEMPERATURE | number | Temperature controls the randomness of text that is generated by LLMs during inference. A value >1 is more creative, <1 is more strict behavior. | 1 |
| TOP_P | number | Top-P sampling selects a group of LLM tokens whose cumulative probability is at least P. | 0.95 |
| TOP_K | number | Top-K sampling limits the LLM to choosing from the K most likely tokens at each step. | 64 |
| NUM_PREDICT | number | The maximum number of tokens the LLM can generate before stopping. | 5000 |
| TIMEOUT_SEC | sec | The maximum time allowed for the LLM before it is stopped. | 600 |
| THINK_MODE | boolean | With think-mode enabled the LLM returns both the reasoning and final answer. | 1 |
| CACHEABLE | boolean | Debug value, when deactivated the LLM Tools can no longer cache wiki pages. | 1 |
| PROJECT_INFO | boolean | Whether to include some basic info about the project that is accessible by the LLM. | 1 |
API Endpoints
References
- ↑ Gemma4 ∙ Found at: https://deepmind.google/models/gemma/gemma-4/ ∙ (last visited: 2026-06-29)
- ↑ Qwen3.8 ∙ Found at: https://qwen.ai/ ∙ (last visited: 2026-09-03)