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POST
/
answer
curl -X POST "http://localhost:8080/answer" \
  -H "Content-Type: application/json" \
  -d '{
    "namespace": "my-documents",
    "query": "What are the main benefits of Moorcheh?",
    "top_k": 5
  }'

{
  "answer": "Serverless architecture offers several benefits...",
  "model": "gpt-4o-mini",
  "context_count": 3,
  "query": "What are the main benefits of using serverless architecture?"
}

​
Overview

Generate AI-powered answers with two modes:
  • Search Mode — provide a namespace name; Moorcheh searches your text namespace and uses retrieved chunks as RAG context
  • Direct AI Mode — set namespace to "" (empty string) for a direct LLM call without retrieval
LLM providers: Ollama, OpenAI, or Cohere. Configure once with moorcheh configure (saved under llm in ~/.moorcheh/config.json). Override the model per request with ai_model.
After changing LLM (or embedding) settings, run moorcheh down then moorcheh up so the running server loads the new config. moorcheh configure does not restart Docker. Check GET /health for llm_provider and llm_model.
If you change the embedding provider or model while you already have text data under ~/.moorcheh/data, RAG answers for existing namespaces may be wrong until you re-upload with the new embeddings. Changing only the answer (LLM) model does not require re-uploading.

​
Headers

​
Content-Type
string
required
Must be application/json

​
Body Parameters

​
query
string
required
The user’s question or query to be answered
​
namespace
string
required
Namespace name for Search Mode, or empty string "" for Direct AI Mode
​
top_k
number
default:"10"
Number of top relevant chunks for your query (Search Mode only). Clamped to 1–100.
​
threshold
number
Minimum relevance score threshold (0–1). Required when kiosk_mode is true.
​
temperature
number
default:"0.7"
AI creativity level (0.0–2.0). Higher = more creative.
​
type
string
default:"text"
Search type for RAG. Only "text" is supported on-prem.
​
ai_model
string
Override the configured LLM model for this request
​
kiosk_mode
boolean
default:"false"
When true, threshold is required and chunks below the threshold are filtered out (Search Mode).
​
chat_history
array
default:"[]"
Previous conversation turns: [{"role":"user"|"assistant","content":"..."}]
​
header_prompt
string
Custom system instruction prepended to the prompt
​
footer_prompt
string
default:"Provide a clear and concise answer."
Custom instruction appended before the user query
​
structured_response
object
When set with enabled: true, the API parses JSON from the model into structured_data. Optional: schema (JSON Schema object).

​
Available LLM models (configure defaults)

ProviderExample model IDsNotes
ollamaqwen2.5, llama3.2, mistralLocal; no API key
openaigpt-5.5, gpt-5, gpt-4o-miniRequires API key in config
coherecommand-a-plus-05-2026, command-r-plus-08-2024, command-r-08-2024Requires API key in config
curl -X POST "http://localhost:8080/answer" \
  -H "Content-Type: application/json" \
  -d '{
    "namespace": "my-documents",
    "query": "What are the main benefits of Moorcheh?",
    "top_k": 5
  }'

{
  "answer": "Serverless architecture offers several benefits...",
  "model": "gpt-4o-mini",
  "context_count": 3,
  "query": "What are the main benefits of using serverless architecture?"
}

​
Response Fields

​
answer
string
The AI-generated answer text
​
model
string
The LLM model ID used for generation
​
context_count
number
Number of context chunks retrieved (Search Mode). 0 in Direct AI Mode.
​
query
string
The original query submitted
​
used_context
boolean
Present when structured_response is enabled: whether RAG context was used.
​
structured_data
object
Parsed JSON when structured_response.enabled is true.

​
Important Notes

  • Search Mode requires a text namespace with indexed documents
  • Direct AI Mode uses only LLM fields (namespace, query, temperature, chat_history, prompts, ai_model, structured_response)
  • Configure LLM provider and default model with moorcheh configure or edit ~/.moorcheh/config.json
  • /health reports llm_provider and llm_model alongside embedding settings