| Region | Image |
|---|---|
| US West | modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/sirchmunk:ubuntu22.04-py312-0.0.7 |
| China Beijing | modelscope-registry.cn-beijing.cr.aliyuncs.com/modelscope-repo/sirchmunk:ubuntu22.04-py312-0.0.7 |
Image tag format: ubuntu{ubuntu_version}-py{python_version}-{sirchmunk_version}
Choose the registry closest to your location:
# US West
docker pull modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/sirchmunk:ubuntu22.04-py312-0.0.7
# China Beijing
docker pull modelscope-registry.cn-beijing.cr.aliyuncs.com/modelscope-repo/sirchmunk:ubuntu22.04-py312-0.0.7docker run -d \
--name sirchmunk \
-p 8584:8584 \
-e LLM_API_KEY="your-api-key-here" \
-e LLM_BASE_URL="https://api.openai.com/v1" \
-e LLM_MODEL_NAME="gpt-5.2" \
-e LLM_TIMEOUT=60.0 \
-e UI_THEME=light \
-e UI_LANGUAGE=en \
-e SIRCHMUNK_VERBOSE=false \
-e SIRCHMUNK_SEARCH_PATHS=/mnt/docs \
-v /path/to/your_work_path:/data/sirchmunk \
-v /path/to/your/docs:/mnt/docs:ro \
modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/sirchmunk:ubuntu22.04-py312-0.0.7Parameters:
| Parameter | Required | Default | Description |
|---|---|---|---|
-e LLM_API_KEY |
Yes | API key from your LLM provider | |
-e LLM_BASE_URL |
No | https://api.openai.com/v1 |
OpenAI-compatible API endpoint (e.g., https://api.minimax.io/v1 for MiniMax) |
-e LLM_MODEL_NAME |
No | gpt-5.2 |
LLM model name (e.g., MiniMax-M2.7, MiniMax-M2.7-highspeed) |
-e LLM_TIMEOUT |
No | 60.0 |
LLM request timeout in seconds |
-e UI_THEME |
No | light |
WebUI theme (light / dark) |
-e UI_LANGUAGE |
No | en |
WebUI language (en / zh) |
-e SIRCHMUNK_VERBOSE |
No | false |
Enable verbose logging (true / false) |
-e SIRCHMUNK_SEARCH_PATHS |
No | (empty) | Comma-separated default search paths (e.g. /mnt/docs) |
-e CHAT_HISTORY_MAX_TURNS |
No | 5 |
Max chat history turns for multi-turn context |
-e CHAT_HISTORY_MAX_TOKENS |
No | 32000 |
Max tokens for chat history context |
-p 8584:8584 |
Yes | Expose WebUI and API port | |
-v /data/sirchmunk:/data/sirchmunk |
Recommended | Persist data (models, history, knowledge) across restarts |
Mount local files for search:
Use -v to mount host directories into the container, then search them via the API or WebUI.
WebUI — Open http://localhost:8584 in your browser.
API — Search via curl:
curl -X POST http://localhost:8584/api/v1/search \
-H "Content-Type: application/json" \
-d '{
"query": "your search question here",
"paths": ["/mnt/docs"],
"mode": "FAST"
}'API — Search via Python:
import requests
response = requests.post(
"http://localhost:8584/api/v1/search",
json={
"query": "your search question here",
"paths": ["/mnt/docs"],
"mode": "FAST", # "FAST" (default), "DEEP", or "FILENAME_ONLY"
"enable_dir_scan": True, # enable directory scanning (DEEP/FAST)
"max_depth": 5, # optional: max directory depth
"top_k_files": 3, # optional: number of top files to return
"max_token_budget": 8192, # optional: LLM token budget (DEEP)
},
)
result = response.json()
if result["success"]:
print(result["data"])
else:
print(f"Error: {result['error']}")Search API fields:
| Field | Type | Required | Description |
|---|---|---|---|
query |
string | Yes | Search query or question |
paths |
list | No | Directories or files to search (e.g., ["/mnt/docs"]); falls back to SIRCHMUNK_SEARCH_PATHS if unset |
mode |
string | No | "FAST" (default, greedy search 2-5s), "DEEP" (comprehensive analysis 10-30s), or "FILENAME_ONLY" (file discovery <1s) |
enable_dir_scan |
bool | No | Enable directory scanning for file discovery in DEEP/FAST (default: true) |
max_depth |
int | No | Maximum directory depth to search |
top_k_files |
int | No | Number of top files to return |
max_token_budget |
int | No | LLM token budget (DEEP mode) |
return_context |
bool | No | Return full SearchContext with KnowledgeCluster and telemetry (default: false) |
# View logs
docker logs -f sirchmunk
# Stop
docker stop sirchmunk
# Restart
docker start sirchmunk
# Remove container (data is preserved in the volume)
docker rm sirchmunk
# Remove data volume (caution: deletes all persisted data)
rm -rf /path/to/your_work_path