Submit chat query

Streams an answer for a document chat: the model uses files already linked to chat_id in Genow (and conversation history for that chat). Responses are Server-Sent Events (text/event-stream).

Authentication: send a valid Bearer JWT (same as other Genow APIs).

Event format: each SSE message is one line starting with data: followed by JSON: {"type": "<TokenType>", "data": ...}. Parse lines that begin with data: ; ignore heartbeats or empty lines as needed.

Token types (type field): the same vocabulary is shared across Genow streaming chat endpoints. For this endpoint (POST /api/chat/ask) you should rely on:

• ANSWER — data is a string fragment of the model reply. Many events are emitted; concatenate data in order to rebuild the full answer.
• FINISHED — stream is complete. data is null. No further answer tokens follow.

Other token types appear on knowledge-asset / search streaming routes, not on document chat ask. Clients should still handle unknown type values defensively:

• SPLIT_SUBTASKS — data is typically a list of strings (sub-questions or pipeline steps) when the router splits work.
• RERANKED_DOCUMENTS — data is typically structured retrieval metadata (e.g. list of document-like objects) after reranking.
• REASONING — data may contain reasoning or intermediate model output when the deployment exposes it.
• FULL_ANSWER — data may carry a full answer payload in one shot on some pipelines (contrast with streamed ANSWER chunks).
• images — data relates to image results when the pipeline returns them (shape depends on caller).

Example SSE fragment (document chat; illustrative):

data: {"type": "ANSWER", "data": "The "}
data: {"type": "ANSWER", "data": "key date is "}
data: {"type": "ANSWER", "data": "2030-01-01."}
data: {"type": "FINISHED", "data": null}

Examples

curl (disable buffering with -N). The llm value must match a chat_model_name from your deployment (below uses Gemini Pro as gemini-2.5-pro, a common LiteLLM id):

curl -N -X POST 'https://<your-genow-host>/api/chat/ask' \
  -H 'Authorization: Bearer <jwt>' \
  -H 'Content-Type: application/json' \
  -d '{
    "query": "What are the key dates in these documents?",
    "llm": "gemini-2.5-pro",
    "chat_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
    "entry_id": "6ba7b810-9dad-11d1-80b4-00c04fd430c8"
  }'

Use the models endpoint to list valid llm values for your environment. Request body examples are also attached to the DocumentChatRequest schema in the OpenAPI document.