SDK 1.3.0-rc3

docs/ContextInput.md

@@ -1,15 +1,14 @@
 # ContextInput
 
-Request body of `POST /api/v2/memory/context`.  The same fields as the `/search` request, except that `method` is fixed to `\"context\"`, `min_score` is not supported, and `radius`, `enable_llm_rerank` and `with_readable_episode` are not accepted.
+Request body of `POST /api/v2/memory/context`.  The same fields as the `/search` request, except that `user_id` is required and `agent_id` is not accepted (user memories only), `method` is fixed to `\"context\"`, `min_score` is not supported, and `radius`, `enable_llm_rerank` and `with_readable_episode` are not accepted.
 
 ## Properties
 
 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] 
+**user_id** | **str** | Required. The user whose memories are searched — matched against the messages' `sender_id`. `agent_id` is not accepted here (422); use /search for agent memories. | 
 **query** | **str** | The question to retrieve for. The loop keeps searching until it judges the evidence sufficient to answer it, so pass the whole question, not keywords. | 
 **method** | **str** | Fixed to `\"context\"`; may be omitted. Any other value is a 422. | [optional] [default to 'context']
 **top_k** | **int** | Maximum number of episodes returned. 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]

docs/MemoryApi.md

@@ -867,7 +867,7 @@ Name | Type | Description  | Notes
 
 Request a memory-space purge (step 1 of 2) [Cloud-only]
 
-**Permanent — there is no undo.** Step 1 of 2: asks for a purge and returns a `nonce`. Nothing is deleted yet.  - `scope: \"space\"` purges the whole memory space; `\"app\"` one app (`app_id`); `\"project\"` one project of one app (`app_id` + `project_id`). - Confirm with `POST /purge/confirm` before `expires_at` (5 minutes). The nonce is single use and only valid for the same memory space and API key.  What is deleted: memories, sessions, messages, memory cells, feedback, events and (space scope only) senders and engine knowledge bases, from every index. Not deleted: files uploaded through `/object/sign`, and data of the separate knowledge-base service.
+**Permanent — there is no undo.** Step 1 of 2: asks for a purge and returns a `nonce`. Nothing is deleted yet.  - `scope: \"space\"` purges the whole memory space; `\"app\"` one app (`app_id`); `\"project\"` one project of one app (`app_id` + `project_id`). - Confirm with `POST /purge/confirm` before `expires_at` (5 minutes). The nonce is single use and only valid for the same memory space and API key.  What is deleted: memories, sessions, messages, memory cells, feedback, events and (space scope only) senders and knowledge bases (`/api/v2/knowledge_bases`: categories, documents, topics), from every index. Left in object storage, no longer reachable through the API: files uploaded through `/object/sign`, and (space scope) the stored text of knowledge base topics.
 
 ### Example
 
@@ -951,7 +951,7 @@ Name | Type | Description  | Notes
 
 Context Retrieval [Cloud-only]
 
-Retrieve memories with a multi-round, LLM-driven search loop.  An LLM plans and runs several searches over the owner's memories — keyword, semantic, hybrid, topic-cluster, and reading the surrounding conversation or a whole session — and keeps searching until it judges the evidence sufficient to answer `query`. The memories it found are returned. **The response contains memories, not an answer**: the model's own answer is discarded.  Use it for questions a single search answers poorly: multi-hop, temporal, or spread across several conversations. For simple lookups, `/search` is faster and cheaper.  **Request** — the same body as `/search`, except: - `method` is fixed to `\"context\"`. It can be omitted; any other value is a 422. - `min_score` is not supported (422): results are ranked by rank fusion, not by a similarity score. - `radius`, `enable_llm_rerank` and `with_readable_episode` are not accepted (422): the loop does not use them.  **Response** — the same `SearchData` shape as `/search`: - The loop returns its memories in `episodes`, for a `user_id` owner only; for an `agent_id` owner `episodes`, `agent_cases` and `agent_skills` are empty. `profiles` (`include_profile`) and `unprocessed_messages` (a top-level `session_id` filter) are filled exactly as on `/search`. - `episodes[].cited` is `true` for memories the model named as the ones its answer rested on. - `episodes[].score` is a rank-fusion score, not a similarity: roughly 0.01–0.05, plus 1.0 for a cited memory, so cited memories come first. It is not comparable with `/search` scores.  **Limits** — by default at most 5 LLM rounds and 45 seconds per request. Reaching a limit is not an error: the memories found so far are returned.  **Latency** — several LLM calls per request, so noticeably slower than `/search`.
+Retrieve memories with a multi-round, LLM-driven search loop.  An LLM plans and runs several searches over the owner's memories — keyword, semantic, hybrid, topic-cluster, and reading the surrounding conversation or a whole session — and keeps searching until it judges the evidence sufficient to answer `query`. The memories it found are returned. **The response contains memories, not an answer**: the model's own answer is discarded.  Use it for questions a single search answers poorly: multi-hop, temporal, or spread across several conversations. For simple lookups, `/search` is faster and cheaper.  **Request** — the same body as `/search`, except: - `user_id` is required and `agent_id` is not accepted (422): the loop searches a user's memories only. Use `/search` for agent memories. - `method` is fixed to `\"context\"`. It can be omitted; any other value is a 422. - `min_score` is not supported (422): results are ranked by rank fusion, not by a similarity score. - `radius`, `enable_llm_rerank` and `with_readable_episode` are not accepted (422): the loop does not use them.  **Response** — the same `SearchData` shape as `/search`: - The loop returns its memories in `episodes`. `profiles` (`include_profile`) and `unprocessed_messages` (a top-level `session_id` filter) are filled exactly as on `/search`. - `episodes[].cited` is `true` for memories the model named as the ones its answer rested on. - `episodes[].score` is a rank-fusion score, not a similarity: roughly 0.01–0.05, plus 1.0 for a cited memory, so cited memories come first. It is not comparable with `/search` scores.  **Limits** — by default at most 5 LLM rounds and 45 seconds per request. Reaching a limit is not an error: the memories found so far are returned.  **Latency** — several LLM calls per request, so noticeably slower than `/search`.
 
 ### Example
 

everos_cloud/__init__.py

@@ -15,7 +15,7 @@
 """  # noqa: E501
 
 
-__version__ = "1.3.0-rc2"
+__version__ = "1.3.0-rc3"
 
 # import apis into sdk package
 from everos_cloud.api.knowledge_api import KnowledgeApi