Bump version to 0.3.1 in pyproject.toml; properly publish

Author: pwilkin

docs/foundational_design.md

@@ -187,7 +187,7 @@ class EmbeddingDefinition(BaseModel):
     name: str = Field(..., description="Unique name for this embedding configuration (e.g., 'content_embedding_v1'). Convention: snake_case.")
     object_type_name: str = Field(..., description="Name of the ObjectTypeDefinition this embedding applies to.")
     source_property_name: str = Field(..., description="Name of the property within the ObjectTypeDefinition whose content will be embedded.")
-    embedding_model: str = Field(default="huggingface/colbert-ir/colbertv2.0", description="Identifier for the embedding model to use.")
+    embedding_model: str = Field(default="huggingface/mixedbread-ai/mxbai-embed-large-v1", description="Identifier for the embedding model to use.")
     dimensions: Optional[int] = Field(default=None, description="Expected dimensions of the embedding vector. If None, inferred from model.")
     description: Optional[str] = Field(default=None, description="Optional description of this embedding definition.")
     # _created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))

docs/testing/e2e_scenario_v1.md

@@ -52,7 +52,7 @@ The following schema will be created using the `Grizabella` client's API methods
     - `name`: "PaperAbstractEmbedding"
     - `object_type_name`: "Paper"
     - `source_property_name`: "abstract"
-    - `embedding_model`: "huggingface/colbert-ir/colbertv2.0" (default, or specify if different)
+    - `embedding_model`: "huggingface/mixedbread-ai/mxbai-embed-large-v1" (default, or specify if different)
     - `description`: "Embedding for the abstract of papers."
 
 ### 3.3. `RelationTypeDefinition`(s)

grizabella/core/models.py

@@ -226,7 +226,7 @@ class EmbeddingDefinition(BaseModel):
 
         embedding_model (str): :noindex: An identifier for the embedding model to be
             used (e.g., a Hugging Face model name like
-            'huggingface/colbert-ir/colbertv2.0').
+            'huggingface/mixedbread-ai/mxbai-embed-large-v1').
 
         dimensions (Optional[int]): :noindex: The expected dimensionality of the
             embedding vector. If None, the system may attempt to infer it
@@ -257,7 +257,7 @@ class EmbeddingDefinition(BaseModel):
         ),
     )
     embedding_model: str = Field(
-        default="huggingface/colbert-ir/colbertv2.0",
+        default="huggingface/mixedbread-ai/mxbai-embed-large-v1",
         description="Identifier for the embedding model to use.",
     )
     dimensions: Optional[int] = Field(

grizabella/ui/dialogs/embedding_definition_dialog.py

@@ -60,7 +60,7 @@ def __init__(self, client: Grizabella, embedding_definition: Optional[EmbeddingD
 
         # Embedding Model
         self.model_label = QLabel("Embedding Model:")
-        self.model_edit = QLineEdit("colbert-ir/colbertv2.0") # Removed "huggingface/" prefix
+        self.model_edit = QLineEdit("mixedbread-ai/mxbai-embed-large-v1") # Removed "huggingface/" prefix
         layout.addWidget(self.model_label)
         layout.addWidget(self.model_edit)
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "grizabella"
-version = "0.2.7"
+version = "0.3.0"
 description = "A tri-layer memory framework for LLM solutions."
 authors = ["Grizabella Project Contributors <contributors@example.com>"]
 readme = "README.md"

tests/e2e/test_grizabella_e2e_scenario.py

@@ -114,7 +114,7 @@ def _define_schema(self):
             name="PaperAbstractEmbedding",
             object_type_name="Paper",
             source_property_name="abstract",
-            embedding_model="colbert-ir/colbertv2.0", # Default from models.py
+            embedding_model="mixedbread-ai/mxbai-embed-large-v1", # Default from models.py
             description="Embedding for the abstract of papers."
         )
         self.client.create_embedding_definition(paper_abstract_ed)