| Name |
Type |
Description |
Notes |
| app_id |
str |
Scope to search in, defaulting to "default". Must match the pair used on write. |
[optional] [default to 'default'] |
| project_id |
str |
Second half of the scope, defaulting to "default". |
[optional] [default to 'default'] |
| user_id |
str |
|
[optional] |
| agent_id |
str |
|
[optional] |
| query |
str |
The natural-language query to retrieve against. |
|
| method |
str |
Retrieval strategy. "keyword" is lexical, "vector" is embedding similarity, "hybrid" (default) combines both, and "agentic" lets the engine run a multi-round LLM-guided retrieval — more thorough, slower. |
[optional] [default to 'hybrid'] |
| top_k |
int |
Maximum number of hits. Either -1 (the default, letting the engine decide) or a value from 1 to 100; anything else is rejected with 422. |
[optional] [default to -1] |
| radius |
float |
|
[optional] |
| min_score |
float |
|
[optional] |
| include_profile |
bool |
Also return the user's profile alongside the hits, saving a second call. Ignored for an agent owner, whose results carry no profiles. |
[optional] [default to False] |
| with_readable_episode |
bool |
Attach a human-readable rendering of each episode to the returned items, for display only — it is not indexed, filterable or scored, and callers fall back to `episode` when it is null. Ignored for an agent owner. |
[optional] [default to False] |
| enable_llm_rerank |
bool |
Opt-in LLM rerank, and only for hybrid agent_case / agent_skill retrieval. The episode hybrid path has its own fact eviction and ignores this, as do keyword, vector and agentic. |
[optional] [default to False] |
| filters |
FilterNode |
|
[optional] |
from everos_cloud.models.search_input import SearchInput
# TODO update the JSON string below
json = "{}"
# create an instance of SearchInput from a JSON string
search_input_instance = SearchInput.from_json(json)
# print the JSON string representation of the object
print(SearchInput.to_json())
# convert the object into a dict
search_input_dict = search_input_instance.to_dict()
# create an instance of SearchInput from a dict
search_input_from_dict = SearchInput.from_dict(search_input_dict)[Back to Model list] [Back to API list] [Back to README]