Bases: VectorStoreSaveLoadMixin, VectorStoreRetrieveMixin, VectorStoreBase
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def __init__(self, **kwargs):
super().__init__(**kwargs)
self._embedder = TfidfEmbedding()
self._distance = CosineDistance()
self.documents = []
|
class-attribute
instance-attribute
type = 'TfidfVectorStore'
instance-attribute
class-attribute
instance-attribute
model_config = ConfigDict(
extra="allow", arbitrary_types_allowed=True
)
class-attribute
instance-attribute
id = Field(default_factory=generate_id)
class-attribute
instance-attribute
class-attribute
instance-attribute
class-attribute
instance-attribute
class-attribute
class-attribute
instance-attribute
class-attribute
instance-attribute
class-attribute
instance-attribute
class-attribute
instance-attribute
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def add_document(self, document: Document) -> None:
self.documents.append(document)
# Recalculate TF-IDF matrix for the current set of documents
self._embedder.fit([doc.content for doc in self.documents])
|
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def add_documents(self, documents: List[Document]) -> None:
self.documents.extend(documents)
# Recalculate TF-IDF matrix for the current set of documents
self._embedder.fit([doc.content for doc in self.documents])
|
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def get_document(self, id: str) -> Union[Document, None]:
for document in self.documents:
if document.id == id:
return document
return None
|
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def get_all_documents(self) -> List[Document]:
return self.documents
|
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def delete_document(self, id: str) -> None:
self.documents = [doc for doc in self.documents if doc.id != id]
# Recalculate TF-IDF matrix for the current set of documents
self._embedder.fit([doc.content for doc in self.documents])
|
update_document(id, updated_document)
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
| def update_document(self, id: str, updated_document: Document) -> None:
for i, document in enumerate(self.documents):
if document.id == id:
self.documents[i] = updated_document
break
# Recalculate TF-IDF matrix for the current set of documents
self._embedder.fit([doc.content for doc in self.documents])
|
Source code in swarmauri_standard/vector_stores/TfidfVectorStore.py
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76 | def retrieve(self, query: str, top_k: int = 5) -> List[Document]:
documents = [query]
documents.extend([doc.content for doc in self.documents])
transform_matrix = self._embedder.fit_transform(documents)
# The inferred vector is the last vector in the transformed_matrix
# The rest of the matrix is what we are comparing
distances = self._distance.distances(
transform_matrix[-1], transform_matrix[:-1]
)
# Get the indices of the top_k most similar (least distant) documents
top_k_indices = sorted(range(len(distances)), key=lambda i: distances[i])[
:top_k
]
return [self.documents[i] for i in top_k_indices]
|
classmethod
Decorator to register a base model in the unified registry.
| RETURNS |
DESCRIPTION |
Callable
|
A decorator function that registers the model class.
TYPE:
Callable[[Type[BaseModel]], Type[BaseModel]]
|
Source code in swarmauri_base/DynamicBase.py
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585 | @classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
"""
Decorator to register a base model in the unified registry.
Returns:
Callable: A decorator function that registers the model class.
"""
def decorator(model_cls: Type[BaseModel]):
"""Register ``model_cls`` as a base model."""
model_name = model_cls.__name__
if model_name in cls._registry:
glogger.warning(
"Model '%s' is already registered; skipping duplicate.", model_name
)
return model_cls
cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
glogger.debug("Registered base model '%s'.", model_name)
DynamicBase._recreate_models()
return model_cls
return decorator
|
classmethod
register_type(resource_type=None, type_name=None)
Decorator to register a subtype under one or more base models in the unified registry.
| PARAMETER |
DESCRIPTION |
resource_type
|
The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
are used.
TYPE:
Optional[Union[Type[T], List[Type[T]]]]
DEFAULT:
None
|
type_name
|
An optional custom type name for the subtype.
TYPE:
Optional[str]
DEFAULT:
None
|
| RETURNS |
DESCRIPTION |
Callable
|
A decorator function that registers the subtype.
TYPE:
Callable[[Type[DynamicBase]], Type[DynamicBase]]
|
Source code in swarmauri_base/DynamicBase.py
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654 | @classmethod
def register_type(
cls,
resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
"""
Decorator to register a subtype under one or more base models in the unified registry.
Parameters:
resource_type (Optional[Union[Type[T], List[Type[T]]]]):
The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
are used.
type_name (Optional[str]): An optional custom type name for the subtype.
Returns:
Callable: A decorator function that registers the subtype.
"""
def decorator(subclass: Type["DynamicBase"]):
"""Register ``subclass`` as a subtype."""
if resource_type is None:
resource_types = [
base for base in subclass.__bases__ if base is not cls
]
elif not isinstance(resource_type, list):
resource_types = [resource_type]
else:
resource_types = resource_type
for rt in resource_types:
if not issubclass(subclass, rt):
raise TypeError(
f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
)
final_type_name = type_name or getattr(
subclass, "_type", subclass.__name__
)
base_model_name = rt.__name__
if base_model_name not in cls._registry:
cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
glogger.debug(
"Created new registry entry for base model '%s'.",
base_model_name,
)
subtypes_dict = cls._registry[base_model_name]["subtypes"]
if final_type_name in subtypes_dict:
glogger.warning(
"Type '%s' already exists under '%s'; skipping duplicate.",
final_type_name,
base_model_name,
)
continue
subtypes_dict[final_type_name] = subclass
glogger.debug(
"Registered '%s' as '%s' under '%s'.",
subclass.__name__,
final_type_name,
base_model_name,
)
DynamicBase._recreate_models()
return subclass
return decorator
|
classmethod
model_validate_toml(toml_data)
Validate a model from a TOML string.
Source code in swarmauri_base/TomlMixin.py
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24 | @classmethod
def model_validate_toml(cls, toml_data: str):
"""Validate a model from a TOML string."""
try:
# Parse TOML into a Python dictionary
toml_content = tomllib.loads(toml_data)
# Convert the dictionary to JSON and validate using Pydantic
return cls.model_validate_json(json.dumps(toml_content))
except tomllib.TOMLDecodeError as e:
raise ValueError(f"Invalid TOML data: {e}")
except ValidationError as e:
raise ValueError(f"Validation failed: {e}")
|
model_dump_toml(
fields_to_exclude=None, api_key_placeholder=None
)
Return a TOML representation of the model.
Source code in swarmauri_base/TomlMixin.py
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56 | def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
"""Return a TOML representation of the model."""
if fields_to_exclude is None:
fields_to_exclude = []
# Load the JSON string into a Python dictionary
json_data = json.loads(self.model_dump_json())
# Function to recursively remove specific keys and handle api_key placeholders
def process_fields(data, fields_to_exclude):
"""Recursively filter fields and apply placeholders."""
if isinstance(data, dict):
return {
key: (
api_key_placeholder
if key == "api_key" and api_key_placeholder is not None
else process_fields(value, fields_to_exclude)
)
for key, value in data.items()
if key not in fields_to_exclude
}
elif isinstance(data, list):
return [process_fields(item, fields_to_exclude) for item in data]
else:
return data
# Filter the JSON data
filtered_data = process_fields(json_data, fields_to_exclude)
# Convert the filtered data into TOML
return toml.dumps(filtered_data)
|
classmethod
model_validate_yaml(yaml_data)
Validate a model from a YAML string.
Source code in swarmauri_base/YamlMixin.py
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23 | @classmethod
def model_validate_yaml(cls, yaml_data: str):
"""Validate a model from a YAML string."""
try:
# Parse YAML into a Python dictionary
yaml_content = yaml.safe_load(yaml_data)
# Convert the dictionary to JSON and validate using Pydantic
return cls.model_validate_json(json.dumps(yaml_content))
except yaml.YAMLError as e:
raise ValueError(f"Invalid YAML data: {e}")
except ValidationError as e:
raise ValueError(f"Validation failed: {e}")
|
model_dump_yaml(
fields_to_exclude=None, api_key_placeholder=None
)
Return a YAML representation of the model.
Source code in swarmauri_base/YamlMixin.py
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55 | def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
"""Return a YAML representation of the model."""
if fields_to_exclude is None:
fields_to_exclude = []
# Load the JSON string into a Python dictionary
json_data = json.loads(self.model_dump_json())
# Function to recursively remove specific keys and handle api_key placeholders
def process_fields(data, fields_to_exclude):
"""Recursively filter fields and apply placeholders."""
if isinstance(data, dict):
return {
key: (
api_key_placeholder
if key == "api_key" and api_key_placeholder is not None
else process_fields(value, fields_to_exclude)
)
for key, value in data.items()
if key not in fields_to_exclude
}
elif isinstance(data, list):
return [process_fields(item, fields_to_exclude) for item in data]
else:
return data
# Filter the JSON data
filtered_data = process_fields(json_data, fields_to_exclude)
# Convert the filtered data into YAML using safe mode
return yaml.safe_dump(filtered_data, default_flow_style=False)
|
model_post_init(logger=None)
Assign a logger instance after model initialization.
Source code in swarmauri_base/LoggerMixin.py
| def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
"""Assign a logger instance after model initialization."""
# Directly assign the provided FullUnion[LoggerBase] or fallback to the
# class-level default.
self.logger = self.logger or logger or self.default_logger
|
Deletes all documents from the vector store
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
| def clear_documents(self) -> None:
"""
Deletes all documents from the vector store
"""
self.documents = []
|
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
| def document_count(self):
return len(self.documents)
|
Placeholder
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
| def document_dumps(self) -> str:
"""
Placeholder
"""
return json.dumps([each.to_dict() for each in self.documents])
|
Placeholder
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
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120 | def document_dump(self, file_path: str) -> None:
"""
Placeholder
"""
with open(file_path, "w", encoding="utf-8") as f:
json.dump(
[each.to_dict() for each in self.documents],
f,
ensure_ascii=False,
indent=4,
)
|
document_loads(json_data)
Placeholder
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
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128 | def document_loads(self, json_data: str) -> None:
"""
Placeholder
"""
self.documents = [
globals()[each["type"]].from_dict(each) for each in json.loads(json_data)
]
|
Placeholder
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
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137 | def document_load(self, file_path: str) -> None:
"""
Placeholder
"""
with open(file_path, "r", encoding="utf-8"):
self.documents = [
globals()[each["type"]].from_dict(each) for each in json.load(file_path)
]
|
save_store(directory_path)
Saves both the vectorizer's model and the documents.
Source code in swarmauri_base/vector_stores/VectorStoreSaveLoadMixin.py
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36 | def save_store(self, directory_path: str) -> None:
"""
Saves both the vectorizer's model and the documents.
"""
# Ensure the directory exists
if not os.path.exists(directory_path):
os.makedirs(directory_path)
# Save the vectorizer model
model_path = os.path.join(directory_path, "embedding_model")
self._vectorizer.save_model(model_path)
# Save documents
documents_path = os.path.join(directory_path, "documents.json")
with open(documents_path, "w", encoding="utf-8") as f:
json.dump(
[each.to_dict() for each in self.documents],
f,
ensure_ascii=False,
indent=4,
)
|
load_store(directory_path)
Loads both the vectorizer's model and the documents.
Source code in swarmauri_base/vector_stores/VectorStoreSaveLoadMixin.py
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49 | def load_store(self, directory_path: str) -> None:
"""
Loads both the vectorizer's model and the documents.
"""
# Load the vectorizer model
model_path = os.path.join(directory_path, "embedding_model")
self.vectorizer.load_model(model_path)
# Load documents
documents_path = os.path.join(directory_path, "documents.json")
with open(documents_path, "r", encoding="utf-8") as f:
self.documents = [self._load_document(each) for each in json.load(f)]
|
save_parts(directory_path, chunk_size=10485760)
Splits the file into parts if it's too large and saves those parts individually.
Source code in swarmauri_base/vector_stores/VectorStoreSaveLoadMixin.py
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87 | def save_parts(self, directory_path: str, chunk_size: int = 10485760) -> None:
"""
Splits the file into parts if it's too large and saves those parts individually.
"""
file_number = 1
model_path = os.path.join(directory_path, "embedding_model")
parts_directory = os.path.join(directory_path, "parts")
if not os.path.exists(parts_directory):
os.makedirs(parts_directory)
with open(f"{model_path}/model.safetensors", "rb") as f:
chunk = f.read(chunk_size)
while chunk:
with open(
f"{parts_directory}/model.safetensors.part{file_number:03}", "wb"
) as chunk_file:
chunk_file.write(chunk)
file_number += 1
chunk = f.read(chunk_size)
# Split the documents into parts and save them
os.path.join(directory_path, "documents")
self._split_json_file(directory_path, chunk_size=chunk_size)
|
load_parts(directory_path, file_pattern='*.part*')
Combines file parts from a directory back into a single file and loads it.
Source code in swarmauri_base/vector_stores/VectorStoreSaveLoadMixin.py
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150 | def load_parts(self, directory_path: str, file_pattern: str = "*.part*") -> None:
"""
Combines file parts from a directory back into a single file and loads it.
"""
model_path = os.path.join(directory_path, "embedding_model")
parts_directory = os.path.join(directory_path, "parts")
output_file_path = os.path.join(model_path, "model.safetensors")
parts = sorted(glob.glob(os.path.join(parts_directory, file_pattern)))
with open(output_file_path, "wb") as output_file:
for part in parts:
with open(part, "rb") as file_part:
output_file.write(file_part.read())
# Load the combined_model now
model_path = os.path.join(directory_path, "embedding_model")
self._vectorizer.load_model(model_path)
# Load document files
self._load_documents(directory_path)
|