Chat
July 15, 2026 ยท View on GitHub
Conversational search request handled by Manticore Buddy via CALL CHAT. When this object is set, the /search endpoint answers through an LLM using KNN-retrieved rows as context instead of returning regular search hits. Required fields are query, table, and model_name. Chat model management (CREATE CHAT MODEL, etc.) remains SQL-only. For more information see Conversational search
Properties
| Name | Type | Description | Notes |
|---|---|---|---|
| query | str | User question to send to the chat model | |
| table | str | Vectorized table to retrieve context from | |
| model_name | str | Name of the chat model | |
| conversation_uuid | str | Existing conversation id to continue the dialog, or an empty string to start a new conversation. If omitted, a new id is generated. | [optional] |
| vector_field | str | A specific vector field to search by. If omitted, Buddy uses the first `FLOAT_VECTOR` field from `SHOW CREATE TABLE`. | [optional] |
| fields | str | Legacy alias for `vector_field`. A request must not include both `vector_field` and `fields`. | [optional] |
Example
from manticoresearch.models.chat import Chat
# TODO update the JSON string below
json = "{}"
# create an instance of Chat from a JSON string
chat_instance = Chat.from_json(json)
# print the JSON string representation of the object
print(Chat.to_json())
# convert the object into a dict
chat_dict = chat_instance.to_dict()
# create an instance of Chat from a dict
chat_from_dict = Chat.from_dict(chat_dict)