Configuration Information
JiuwenSwarm configuration serves as the foundational setup for your interactions with the agent. Proper configuration allows you to connect to various model services, enable multimodal capabilities, integrate third-party services, and adjust system behavior parameters.
This document details each configuration option in the JiuwenSwarm frontend panel to help you get started quickly and fully leverage the system's capabilities.
1. Configuration entry
Open Configuration from the left navigation bar to view and edit settings for models, third-party services, free search, and more. Click Save after changes; whether you need to wait for services to become ready depends on your deployment.
The configuration panel contains the following main sections:
- Model Configuration: Default chat model, video/audio/vision models (see 2. Model Configuration)
- Embedding Configuration: Vector embedding service (see 3. Embedding Configuration)
- Third-Party Services: Jina, Bocha, Serper, Perplexity, GitHub, etc. (see 4. Third-Party Service Configuration)
- Self-Evolution Configuration: Automatic skill improvement (see 5. Self-Evolution Configuration)
- Context Compression: Dialogue history management (see 6. Context Compression)
- Tool Security Guardrails: Tool invocation permission checks (see 7. Tool Security Guardrails)
💡 Tip: Model configuration (
api_base,api_key,model,model_provider) is required; all other configurations are optional.
2. Model Configuration
Before using JiuwenSwarm, you must obtain an API key from your chosen model provider. Visit the provider's official website and follow instructions to apply for an API key.
2.1 Supported Model Types
JiuwenSwarm supports multiple model types to meet diverse scenario requirements:
| Model Type | Purpose | Capability Requirements | Required |
|---|---|---|---|
| Default Model | Core dialogue model; handles text chat, task planning, tool calling, etc | Must support Function Calling and multi-turn dialogue | ✅ Yes |
| Video Model | Video understanding and analysis; supports video Q&A, scene detection | Must support video understanding and process video input | ⭕ No |
| Audio Model | Speech recognition and processing; supports ASR, audio content analysis | Must support speech recognition / audio understanding | ⭕ No |
| Vision Model | Image understanding and analysis; supports image Q&A, OCR, captioning | Must support image understanding and process image input | ⭕ No |
| Image Generation Model | Generate images from text descriptions; supports AI painting, image creation | Must support image generation and create images from text | ⭕ No |
💡 Tip: The default model is essential for system operation and must be configured correctly. Video, audio, vision, and image generation models are optional; configure them only when multimodal capabilities are needed.
2.2 Configuration Fields
Each model type supports the following parameters:
| Field | Description | Remarks |
|---|---|---|
api_base |
Base URL for model API | Use the provider's API endpoint; do not include /chat/completions; appended automatically |
api_key |
Model API key | Obtained from the model provider; keep confidential |
model |
Model identifier | Use exact model ID such as gpt-4o, claude-3-opus, deepseek-chat |
model_provider |
Model provider type | Supports OpenAI, Azure, SiliconFlow, etc., for API format adaptation |
💡 Test Function: The configuration panel provides a Test button. After filling in the model configuration, you can click "Test" to verify the API connection. The system will send a simple test request and display "Test Successful" if successful, or show error information otherwise.
Configuration Examples
OpenAI-compatible API
api_base: https://api.openai.com/v1
api_key: sk-your-openai-api-key
model: gpt-4o
model_provider: OpenAI
💡 Tip: Most model providers offer OpenAI-compatible APIs. You can adjust
api_baseandmodelparameters based on your actual provider.
2.3 Multi-Model Management and Aliases
The Model List section in the configuration panel supports maintaining multiple models simultaneously for quick switching between different models.
Each model entry contains the following fields:
| Field | Required | Description |
|---|---|---|
model_name |
Yes | Model name at the API layer (e.g., gpt-4o, deepseek-chat) |
alias |
No | Display name / switch identifier; defaults to model_name if empty |
api_base |
Yes | API endpoint for this model |
api_key |
Yes | API key for this model |
model_provider |
Yes | Provider (e.g., OpenAI, DeepSeek) |
temperature |
No | Sampling temperature, default 0.95 |
alias Rules:
- If empty, it automatically defaults to
model_namewhen saved; - Must be globally unique across all configured models: cannot duplicate another model's
aliasormodel_name; - When switching models (Web dropdown / CLI
\model <name>), you can use eitheraliasormodel_nameas the identifier.
The first item in the list is the default model; you can drag to reorder or click "Set as Default" to change the default.
2.4 Multimodal Model Usage Examples
Once video, audio, or vision models are configured, JiuwenSwarm enables corresponding multimodal features automatically.
Video Model
Using GLM-4.6V-Flash11 video understanding API as an example:
api_base: https://open.bigmodel.cn/api/paas/v4
api_key: your-zhipu-api-key
model: GLM-4.6V-Flash11
model_provider: ZhiPu
When you send a video file to JiuwenSwarm, the system will invoke the video model for analysis:
User: Analyze this video and list the main scenes.
[Attachment: meeting_recording.mp4]
JiuwenSwarm: Based on video analysis, the main scenes are:
1. Opening remarks (0:00–2:30)
2. Project progress report (2:30–8:15)
3. Issue discussion (8:15–12:00)
4. Summary and next steps (12:00–15:00)
Audio Model
Using GLM-ASR-2512 audio model as an example:
api_base: https://open.bigmodel.cn/api/paas/v4
api_key: your-zhipu-api-key
model: glm-asr-2512
model_provider: ZhiPu
When you send an audio file, the system will invoke the audio model for speech recognition or analysis:
User: Transcribe this audio recording.
[Attachment: voice_message.m4a]
JiuwenSwarm: Transcription:
"Project review meeting at 3 PM tomorrow in Conference Room B. Please prepare materials in advance..."
Vision Model
Using GLM-4.6V-Flash11 vision model as an example:
api_base: https://open.bigmodel.cn/api/paas/v4
api_key: your-zhipu-api-key
model: GLM-4.6V-Flash11
model_provider: ZhiPu
User: Extract data from the table in this image.
[Attachment: data_chart.png]
JiuwenSwarm: Extracted sales data:
- January: 1.2M
- February: 1.35M
- March: 1.48M
Showing an upward trend...
Image Generation Model
Using GLM-4.6V-Flash11 image generation model as an example:
api_base: https://open.bigmodel.cn/api/paas/v4
api_key: your-zhipu-api-key
model: GLM-4.6V-Flash11
model_provider: ZhiPu
When you request image generation, the system will invoke the image generation model:
User: Generate an image of a beach at sunset.
JiuwenSwarm: [Generated image]
Generated an image of a beach at sunset, golden sunlight sparkling on the shimmering sea...
3. Embedding Configuration
Embedding models convert text into vector representations and form the core of JiuwenSwarm's memory system for semantic retrieval.
3.1 Purpose
- Semantic search: Vectorize memory content for similarity-based retrieval rather than simple keyword matching
- Memory recall: Improve accuracy when querying historical information
- Hybrid retrieval: Combine with BM25 full-text search for optimal recall
💡 Tip: Embedding configuration is optional. If not set, the system uses a mock provider for basic retrieval. Configuring an embedding model improves semantic search precision. See the Memory documentation for details.
3.2 Configuration Fields
| Field | Description | Remarks |
|---|---|---|
embed_api_base |
Base URL for embedding API | Embedding service API endpoint |
embed_api_key |
Embedding service API key | Obtained from the service provider |
embed_model |
Embedding model name | Chinese-optimized embedding recommended |
Configuration Example
SiliconFlow
embed_api_base: https://api.siliconflow.cn/v1
embed_api_key: sk-your-siliconflow-api-key
embed_model: BAAI/bge-large-zh-v1.5
4. Third-Party Service Configuration
This section mirrors §1.4 and §1.5 for readers who jump here first. All items below appear on the Configuration page (all optional).
| Field | Description | Reference |
|---|---|---|
jina_api_key |
Jina; fetch and some search flows | Jina |
bocha_api_key |
Bocha Web Search | Bocha Open Platform |
serper_api_key |
Serper | Serper |
perplexity_api_key |
Perplexity | Perplexity |
github_token |
GitHub; SkillNet, etc. | GitHub tokens |
teamskills_hub_token |
TeamSkillsHub user token | TeamSkillsHub |
⚠️ Note: All optional. If unset, related features may be unavailable or fall back; exact behavior depends on your product version.
5. Self-Evolution Configuration
Self-evolution controls the automatic improvement of JiuwenSwarm's Skills.
Toggle
- Field:
evolution.enabled - Default:
false(disabled) - Purpose: When enabled, the system detects issues in Skill execution and generates improvement suggestions to continuously optimize performance.
When enabled, the system will:
- Monitor Skill execution and dialogue history
- Detect execution failures, user feedback, and improvement signals
- Automatically generate and log refinement suggestions
📖 For details on the self-evolution mechanism, see Skill self-evolution.
6. Context Compression
Context compression manages dialogue history retention strategies.
Toggle
- Field:
context_engine.enabled - Default:
true(enabled) - Purpose: Automatically compress and offload dialogue history when exceeding context window limits to maintain fluent interaction.
When enabled, the system will:
- Monitor message count and token usage
- Archive low-priority content when thresholds are reached
- Preserve lightweight indexes to free space for ongoing tasks
📖 For details, see Context Compression & Offloading.
7. Tool Security Guardrails
Security guardrails enforce permission checks during tool invocation.
Toggle
- Field:
permissions.enabled - Default:
false(disabled) - Purpose: When enabled, the system performs permission checks before sensitive tool operations and follows policies to allow, prompt for confirmation, or deny actions.
When enabled, the system will:
- Check permission rules for each tool call
- Resolve action:
allow,ask, ordeny - Prompt user confirmation for
ask-classified operations
Example Permission Rules
permissions:
enabled: true
defaults:
"*": "allow" # Allow all actions by default
tools:
mcp_exec_command:
"*": "ask" # Require confirmation for command execution
patterns:
- pattern: "rm -rf"
action: "deny" # Reject dangerous commands
8. Advanced Configuration
Beyond the Configuration page, the product may use a main configuration for timeouts, temperature, heartbeat interval, context thresholds, and toggles that work together with context compression, permissions, memory restrictions, and similar UI switches. This document does not state where those files live on disk; for offline edits or bulk rollout, contact your administrator.
8.1 Common logical keys (conceptual paths)
These are conceptual paths in the main configuration for cross-reference with ops or release notes; they are not a one-to-one list of every UI field.
| Item (conceptual path) | Description | Typical default |
|---|---|---|
preferred_language |
UI language | zh |
models.*.model_client_config.timeout |
Model request timeout (seconds) | 1800 |
models.*.model_client_config.verify_ssl |
Verify SSL | false |
models.*.model_config_obj.temperature |
Temperature | 0.95 |
heartbeat.every |
Heartbeat interval (seconds) | 3600 |
context_engine.max_messages |
Message count threshold | 100 |
context_engine.max_tokens |
Token threshold | 100000 |
8.2 Runtime parameters outside the Configuration page
Fine-grained options for browser automation, network proxies, or some search paths may be supplied by the runtime or deployment template and may not appear on the Configuration page. Typical users only need required UI fields and business keys; leave the rest to admins or ops.
8.3 Precedence (conceptual)
Generally, from highest to lowest: values you save in the web Configuration UI → environment-injected variables → built-in product defaults. Exact behavior depends on your version and deployment.
💡 Tip: If changes do not seem to apply immediately, wait briefly or ask an admin whether services have reloaded.
9. FAQ
Q: Configurations not taking effect after saving?
A: The backend restarts automatically after saving. Please wait a few moments and retry. If issues persist, verify configuration format correctness.
Q: How to view the currently active model?
A: Model information is displayed on the configuration panel. You may also check system logs for the actual model being called.
Q: Are multimodal models required?
A: No. Video, audio, and vision models are optional and only required for their respective multimodal functions.
Q: Which embedding model is recommended?
A: BAAI/bge-large-zh-v1.5 is recommended for high-quality Chinese embeddings. Other models may be selected based on language requirements.
Q: How to test if the configured model is working?
A: After configuration, you can test the model with these methods:
- Send a simple message: Send a simple message like "Hello" via the web frontend and check if you receive a normal response
- Check logs: Review backend logs to confirm successful model calls without errors
- Test multimodal: If multimodal models are configured, send images/audio/video to test
Q: How to troubleshoot configuration errors?
A: When configuration issues occur, follow these steps:
- Check API Key: Verify the API Key is correct, not expired, and has sufficient quota
- Check API Base: Verify the API address is correct, note that
/chat/completionssuffix should not be included - Check Model Name: Verify the model name is correct, different providers may have different naming conventions
- View Logs: Backend logs will show specific error messages like authentication failure, model not found, etc.
Common errors and solutions:
| Error Message | Possible Cause | Solution |
|---|---|---|
401 Unauthorized |
Invalid or expired API Key | Check and update API Key |
404 Not Found |
Incorrect API address or model name | Check api_base and model configuration |
429 Too Many Requests |
Rate limit exceeded | Wait and retry, or upgrade plan |
Connection Error |
Network issue or API address unreachable | Check network connection and API address |