fix(agents-api, memory-store): Add migration for delete cascade + search docs fixes

agents-api/agents_api/autogen/Docs.py

@@ -153,7 +153,7 @@ class HybridDocSearchRequest(BaseDocSearchRequest):
     model_config = ConfigDict(
         populate_by_name=True,
     )
-    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0.5
+    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0
     """
     The confidence cutoff level
     """
@@ -222,7 +222,7 @@ class VectorDocSearchRequest(BaseDocSearchRequest):
     model_config = ConfigDict(
         populate_by_name=True,
     )
-    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0.5
+    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0
     """
     The confidence cutoff level
     """

agents-api/agents_api/queries/docs/get_doc.py

@@ -44,9 +44,9 @@
 
 
 def transform_get_doc(d: dict) -> dict:
-    content = d["content"][0] if len(d["content"]) == 1 else d["content"]
+    content = d["content"]
 
-    embeddings = d["embeddings"][0] if len(d["embeddings"]) == 1 else d["embeddings"]
+    embeddings = d["embeddings"]
 
     if isinstance(embeddings, str):
         embeddings = json.loads(embeddings)

agents-api/agents_api/queries/docs/list_docs.py

@@ -52,9 +52,9 @@
 
 
 def transform_list_docs(d: dict) -> dict:
-    content = d["content"][0] if len(d["content"]) == 1 else d["content"]
+    content = d["content"]
 
-    embeddings = d["embeddings"][0] if len(d["embeddings"]) == 1 else d["embeddings"]
+    embeddings = d["embeddings"]
 
     if isinstance(embeddings, str):
         embeddings = json.loads(embeddings)

agents-api/agents_api/queries/docs/search_docs_by_embedding.py

@@ -74,7 +74,7 @@ async def search_docs_by_embedding(
             owner_types,
             owner_ids,
             k,
-            confidence,
+            1.0 - confidence,
             metadata_filter,
         ],
     )

agents-api/agents_api/queries/docs/search_docs_hybrid.py

@@ -91,7 +91,7 @@ async def search_docs_hybrid(
             owner_ids,
             k,
             alpha,
-            confidence,
+            1.0 - confidence,
             metadata_filter,
             search_language,
         ],

integrations-service/integrations/autogen/Docs.py

@@ -153,7 +153,7 @@ class HybridDocSearchRequest(BaseDocSearchRequest):
     model_config = ConfigDict(
         populate_by_name=True,
     )
-    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0.5
+    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0
     """
     The confidence cutoff level
     """
@@ -222,7 +222,7 @@ class VectorDocSearchRequest(BaseDocSearchRequest):
     model_config = ConfigDict(
         populate_by_name=True,
     )
-    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0.5
+    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0
     """
     The confidence cutoff level
     """

memory-store/migrations/000020_executions_task_cascade.down.sql

@@ -0,0 +1,13 @@
+BEGIN;
+
+-- Remove cascading foreign key constraint
+ALTER TABLE executions
+    DROP CONSTRAINT IF EXISTS fk_executions_task;
+
+-- Restore original foreign key without cascading behavior
+ALTER TABLE executions
+    ADD CONSTRAINT fk_executions_task
+    FOREIGN KEY (developer_id, task_id, task_version)
+    REFERENCES tasks (developer_id, task_id, "version");
+
+COMMIT;

memory-store/migrations/000020_executions_task_cascade.up.sql

@@ -0,0 +1,14 @@
+BEGIN;
+
+-- Temporarily remove foreign key to modify its behavior
+ALTER TABLE executions
+    DROP CONSTRAINT IF EXISTS fk_executions_task;
+
+-- Recreate foreign key with cascading deletes
+ALTER TABLE executions
+    ADD CONSTRAINT fk_executions_task
+    FOREIGN KEY (developer_id, task_id, task_version)
+    REFERENCES tasks (developer_id, task_id, "version")
+    ON DELETE CASCADE;
+
+COMMIT;

typespec/docs/models.tsp

@@ -127,7 +127,7 @@ model VectorDocSearchRequest extends BaseDocSearchRequest {
     /** The confidence cutoff level */
     @minValue(0)
     @maxValue(1)
-    confidence: float = 0.5;
+    confidence: float = 0;
 
     /** Vector to use in the search. Must be the same dimensions as the embedding model or else an error will be thrown. */
     vector: float[];
@@ -148,7 +148,7 @@ model HybridDocSearchRequest extends BaseDocSearchRequest {
     /** The confidence cutoff level */
     @minValue(0)
     @maxValue(1)
-    confidence: float = 0.5;
+    confidence: float = 0;
 
     /** The weight to apply to BM25 vs Vector search results. 0 => pure BM25; 1 => pure vector; */
     @minValue(0)

typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml

@@ -2986,7 +2986,7 @@ components:
           minimum: 0
           maximum: 1
           description: The confidence cutoff level
-          default: 0.5
+          default: 0
         alpha:
           type: number
           minimum: 0
@@ -3069,7 +3069,7 @@ components:
           minimum: 0
           maximum: 1
           description: The confidence cutoff level
-          default: 0.5
+          default: 0
         vector:
           type: array
           items: