Bases: VectorStoreSaveLoadMixin
, VectorStoreRetrieveMixin
, VectorStoreBase
A vector store implementation using DuckDB as the backend.
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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63 | def __init__(self, **data):
super().__init__(**data)
self._embedder = Doc2VecEmbedding()
self._distance = CosineDistance()
if not os.path.exists(self.persist_dir):
os.makedirs(self.persist_dir)
if self.database_name == ":memory:":
self._conn = duckdb.connect(self.database_name)
self._setup_extensions(self._conn)
self._initialize_table(self._conn)
else:
self._database_path = os.path.join(self.persist_dir, self.database_name)
with duckdb.connect(self._database_path) as conn:
self._setup_extensions(conn)
self._initialize_table(conn)
|
type
class-attribute
instance-attribute
type = 'DuckDBVectorStore'
database_name
class-attribute
instance-attribute
database_name = Field(
default=":memory:",
description="Name of the DuckDB database",
)
table_name
class-attribute
instance-attribute
table_name = Field(
default="documents",
description="Name of the table to store documents",
)
embed_dim
class-attribute
instance-attribute
embed_dim = Field(
default=None,
description="Dimension of the embedding vectors",
)
persist_dir
class-attribute
instance-attribute
persist_dir = Field(
default="./storage",
description="Directory to persist the database",
)
model_config
class-attribute
instance-attribute
model_config = ConfigDict(
extra="allow", arbitrary_types_allowed=True
)
id
class-attribute
instance-attribute
id = Field(default_factory=generate_id)
members
class-attribute
instance-attribute
owners
class-attribute
instance-attribute
host
class-attribute
instance-attribute
default_logger
class-attribute
logger
class-attribute
instance-attribute
name
class-attribute
instance-attribute
resource
class-attribute
instance-attribute
version
class-attribute
instance-attribute
documents
class-attribute
instance-attribute
connect
Connect to the DuckDB database and initialize if necessary.
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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88 | def connect(self) -> None:
"""Connect to the DuckDB database and initialize if necessary."""
if not self._is_initialized:
if self.database_name == ":memory:":
self._initialize_table(self._conn)
else:
with duckdb.connect(self._database_path) as conn:
self._setup_extensions(conn)
self._initialize_table(conn)
self._is_initialized = True
|
disconnect
Disconnect from the DuckDB database.
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
| def disconnect(self) -> None:
"""Disconnect from the DuckDB database."""
if self._conn and self.database_name == ":memory:":
self._conn.close()
self._conn = None
self._is_initialized = False
|
add_document
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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146 | def add_document(self, document: Document) -> None:
# Ensure the document is properly prepared before insertion
data = self._prepare_document(document)
query = f"""
INSERT OR REPLACE INTO {self.table_name} (id, content, embedding, metadata)
VALUES (?, ?, ?, ?)
"""
if self.database_name == ":memory:":
self._conn.execute(
query,
[data["id"], data["content"], data["embedding"], data["metadata"]],
)
else:
with duckdb.connect(self._database_path) as conn:
conn.execute(
query,
[data["id"], data["content"], data["embedding"], data["metadata"]],
)
|
add_documents
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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171 | def add_documents(self, documents: List[Document]) -> None:
ids = [doc.id for doc in documents]
contents = [doc.content for doc in documents]
self._embedder.fit(contents) # Fit the embedder once with all contents
embeddings = [
self._embedder.transform([doc.content])[0].to_numpy().tolist()
for doc in documents
]
metadatas = [json.dumps(doc.metadata or {}) for doc in documents]
data_list = list(zip(ids, contents, embeddings, metadatas))
query = f"""
INSERT OR REPLACE INTO {self.table_name} (id, content, embedding, metadata)
VALUES (?, ?, ?, ?)
"""
if self.database_name == ":memory:":
self._conn.executemany(query, data_list)
else:
with duckdb.connect(self._database_path) as conn:
conn.executemany(query, data_list)
|
get_document
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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185 | def get_document(self, id: str) -> Optional[Document]:
query = f"SELECT id, content, metadata FROM {self.table_name} WHERE id = ?"
if self.database_name == ":memory:":
result = self._conn.execute(query, [id]).fetchone()
else:
with duckdb.connect(self._database_path) as conn:
result = conn.execute(query, [id]).fetchone()
if result:
return Document(
id=result[0], content=result[1], metadata=json.loads(result[2])
)
return None
|
retrieve
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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223 | def retrieve(self, query: str, top_k: int = 5) -> List[Document]:
query_embedding = self._embedder.transform([query])[0].to_numpy().tolist()
select_query = f"""
SELECT id, content, metadata, embedding
FROM {self.table_name}
"""
if self.database_name == ":memory:":
results = self._conn.execute(select_query).fetchall()
else:
with duckdb.connect(self._database_path) as conn:
results = conn.execute(select_query).fetchall()
# Calculate cosine similarities
similarities = [
(
row[0],
row[1],
row[2],
row[3],
self._cosine_similarity(query_embedding, row[3]),
)
for row in results
]
# Get top-k results sorted by similarity
top_results = sorted(similarities, key=lambda x: x[4], reverse=True)[:top_k]
return [
Document(
id=row[0],
content=row[1],
metadata=json.loads(row[2]),
embedding=Vector(value=row[3]),
)
for row in top_results
]
|
delete_document
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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231 | def delete_document(self, id: str) -> None:
query = f"DELETE FROM {self.table_name} WHERE id = ?"
if self.database_name == ":memory:":
self._conn.execute(query, [id])
else:
with duckdb.connect(self._database_path) as conn:
conn.execute(query, [id])
|
get_all_documents
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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244 | def get_all_documents(self) -> List[Document]:
query = f"SELECT id, content, metadata FROM {self.table_name}"
if self.database_name == ":memory:":
results = self._conn.execute(query).fetchall()
else:
with duckdb.connect(self._database_path) as conn:
results = conn.execute(query).fetchall()
return [
Document(id=row[0], content=row[1], metadata=json.loads(row[2]))
for row in results
]
|
update_document
update_document(id, new_document)
Update an existing document in the DuckDB store.
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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266 | def update_document(self, id: str, new_document: Document) -> None:
"""Update an existing document in the DuckDB store."""
try:
data = self._prepare_document(new_document)
query = f"""
UPDATE {self.table_name}
SET content = ?, embedding = ?, metadata = ?
WHERE id = ?
"""
if self.database_name == ":memory:":
self._conn.execute(
query, [data["content"], data["embedding"], data["metadata"], id]
)
else:
with duckdb.connect(self._database_path) as conn:
conn.execute(
query,
[data["content"], data["embedding"], data["metadata"], id],
)
except Exception as e:
raise RuntimeError(f"Failed to update document {id}: {str(e)}")
|
from_local
classmethod
from_local(database_path, table_name='documents', **kwargs)
Load a DuckDBVectorStore from a local file.
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
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278 | @classmethod
def from_local(cls, database_path: str, table_name: str = "documents", **kwargs):
"""Load a DuckDBVectorStore from a local file."""
database_name = os.path.basename(database_path)
persist_dir = os.path.dirname(database_path)
return cls(
database_name=database_name,
table_name=table_name,
persist_dir=persist_dir,
**kwargs,
)
|
model_dump_json
model_dump_json(*args, **kwargs)
Override model_dump_json to ensure connection is closed before serialization.
Source code in swarmauri_vectorstore_duckdb/DuckDBVectorStore.py
| def model_dump_json(self, *args, **kwargs) -> str:
"""Override model_dump_json to ensure connection is closed before serialization."""
self.disconnect()
return super().model_dump_json(*args, **kwargs)
|
register_model
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
|
register_type
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
|
model_validate_toml
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
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)
|
model_validate_yaml
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
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
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
|
clear_documents
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 = []
|
document_count
Source code in swarmauri_base/vector_stores/VectorStoreBase.py
| def document_count(self):
return len(self.documents)
|
document_dumps
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])
|
document_dump
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
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)
]
|
document_load
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
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
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
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
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)
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