Class swarmauri_vectorstore_redis.RedisVectorStore.RedisVectorStore

swarmauri_vectorstore_redis.RedisVectorStore.RedisVectorStore

RedisVectorStore(**kwargs)

Bases: VectorStoreSaveLoadMixin, VectorStoreRetrieveMixin, VectorStoreBase

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def __init__(self, **kwargs):
    super().__init__(**kwargs)
    self._embedder = Doc2VecEmbedding(vector_size=self.embedding_dimension)

    # Initialize Redis client using class attributes
    self.connect()

    # Setup Redis Search index
    vector_field = VectorField(
        "embedding",
        "FLAT",
        {
            "TYPE": "FLOAT32",
            "DIM": self.embedding_dimension,
            "DISTANCE_METRIC": "COSINE",
        },
    )
    text_field = TextField("content")

    try:
        self._redis_client.ft(self.index_name).info()
        print(f"Index '{self.index_name}' exists.")
    except Exception:
        print(f"Index '{self.index_name}' does not exist. Creating index...")
        schema = (text_field, vector_field)
        definition = IndexDefinition(prefix=["doc:"], index_type=IndexType.HASH)
        self._redis_client.ft(self.index_name).create_index(
            fields=schema, definition=definition
        )
        print(f"Index '{self.index_name}' created successfully.")

type class-attribute instance-attribute

type = 'RedisVectorStore'

index_name class-attribute instance-attribute

index_name = 'documents_index'

embedding_dimension class-attribute instance-attribute

embedding_dimension = 8000

redis_host class-attribute instance-attribute

redis_host = 'localhost'

redis_port class-attribute instance-attribute

redis_port = 6379

redis_password class-attribute instance-attribute

redis_password = None

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

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = Field(default=VECTOR_STORE.value)

version class-attribute instance-attribute

version = '0.1.0'

documents class-attribute instance-attribute

documents = []

embedder property

embedder

connect

connect()

Establishes a connection to the Redis server using class attributes.

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def connect(self) -> None:
    """
    Establishes a connection to the Redis server using class attributes.
    """
    try:
        self._redis_client = redis.Redis(
            host=self.redis_host,
            port=self.redis_port,
            password=self.redis_password,
            decode_responses=False,  # For binary data
        )
        # Test the connection
        self._redis_client.ping()
        print("Connected to Redis successfully.")
    except Exception as e:
        print(f"Failed to connect to Redis: {e}")
        raise

disconnect

disconnect()

Disconnects from the Redis server.

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def disconnect(self) -> None:
    """
    Disconnects from the Redis server.
    """
    if self._redis_client:
        self._redis_client.close()
        self._redis_client = None
        print("Disconnected from Redis.")

add_document

add_document(document)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def add_document(self, document: Document) -> None:
    doc = document
    pipeline = self._redis_client.pipeline()

    # Embed the document content
    embedding = self._embedder.fit_transform([doc.content])[0]

    if isinstance(embedding, Vector):
        embedding = embedding.value
    metadata = doc.metadata

    # print("METADATA ::::::::::::::::::::", metadata)
    doc_key = self._doc_key(doc.id)
    # print("DOC KEY ::::::::::::::::::::", doc_key)
    pipeline.hset(
        doc_key,
        mapping={
            "content": doc.content,
            "metadata": json.dumps(metadata),  # Store metadata as JSON
            "embedding": np.array(
                embedding, dtype=np.float32
            ).tobytes(),  # Convert embedding values to bytes
        },
    )
    pipeline.execute()

add_documents

add_documents(documents)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def add_documents(self, documents: List[Document]) -> None:
    pipeline = self._redis_client.pipeline()
    for doc in documents:
        if not doc.content:
            continue
        # Embed the document content
        embedding = self._embedder.fit_transform([doc.content])[0]

        if isinstance(embedding, Vector):
            embedding = embedding.value
        metadata = {doc.metadata}

        doc_key = self._doc_key(doc.id)
        pipeline.hset(
            doc_key,
            mapping={
                "content": doc.content,
                "metadata": json.dumps(metadata),
                "embedding": np.array(embedding, dtype=np.float32).tobytes(),
            },
        )
    pipeline.execute()

get_document

get_document(id)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def get_document(self, id: str) -> Union[Document, None]:
    doc_key = self._doc_key(id)
    data = self._redis_client.hgetall(doc_key)
    if not data:
        return None

    metadata_raw = data.get(b"metadata", b"{}").decode("utf-8")
    metadata = json.loads(metadata_raw)

    content = data.get(b"content", b"").decode("utf-8")
    # print("METAAAAAAA ::::::::::::", metadata)

    embedding_bytes = data.get(b"embedding")
    if embedding_bytes:
        embedding = Vector(
            value=np.frombuffer(embedding_bytes, dtype=np.float32).tolist()
        )
    else:
        embedding = None
    return Document(id=id, content=content, metadata=metadata, embedding=embedding)

get_all_documents

get_all_documents()

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def get_all_documents(self) -> List[Document]:
    cursor = "0"
    documents = []
    while cursor != 0:
        cursor, keys = self._redis_client.scan(
            cursor=cursor, match="doc:*", count=1000
        )
        for key in keys:
            data = self._redis_client.hgetall(key)
            if not data:
                continue
            doc_id = key.decode("utf-8").split("doc:")[1]
            metadata_raw = data.get(b"metadata", b"{}").decode("utf-8")
            metadata = json.loads(metadata_raw)
            content = data.get(b"content", b"").decode("utf-8")
            embedding_bytes = data.get(b"embedding")
            if embedding_bytes:
                embedding = Vector(
                    value=np.frombuffer(embedding_bytes, dtype=np.float32).tolist()
                )
            else:
                embedding = None
            document = Document(
                id=doc_id, content=content, metadata=metadata, embedding=embedding
            )
            documents.append(document)
    return documents

delete_document

delete_document(id)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def delete_document(self, id: str) -> None:
    doc_key = self._doc_key(id)
    self._redis_client.delete(doc_key)

update_document

update_document(document)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def update_document(self, document: Document) -> None:
    doc_key = self._doc_key(document.id)
    if not self._redis_client.exists(doc_key):
        raise ValueError(f"Document with id {document.id} does not exist.")
    # Update the document by re-adding it
    self.add_documents([document])

cosine_similarity

cosine_similarity(vec1, vec2)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def cosine_similarity(self, vec1, vec2):
    dot_product = np.dot(vec1, vec2)
    norm_vec1 = np.linalg.norm(vec1)
    norm_vec2 = np.linalg.norm(vec2)
    if norm_vec1 == 0 or norm_vec2 == 0:
        return 0
    return dot_product / (norm_vec1 * norm_vec2)

retrieve

retrieve(query, top_k=5)

Source code in swarmauri_vectorstore_redis/RedisVectorStore.py

def retrieve(self, query: str, top_k: int = 5) -> List[Document]:
    query_vector = self._embedder.infer_vector(query)

    all_documents = self.get_all_documents()
    # print("ALL DOCUMENTS ::::::::::::::::::::", all_documents[:10])
    similarities = []
    for doc in all_documents:
        if doc.embedding is not None:
            doc_vector = doc.embedding
            # print("DOC VECTOR ::::::::::::::::::::", doc_vector.value[:10])
            similarity = self.cosine_similarity(
                query_vector.value, doc_vector.value
            )
            similarities.append((doc, similarity))

    similarities.sort(key=lambda x: x[1], reverse=True)
    # print("SIMILARITIES ::::::::::::::::::::", similarities[:10])
    top_documents = [doc for doc, _ in similarities[:top_k]]
    # print(f"Found {len(top_documents)} similar documents.")
    return top_documents

register_model classmethod

register_model()

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

@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

@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

@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

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

@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

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

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

document_count()

Source code in swarmauri_base/vector_stores/VectorStoreBase.py

def document_count(self):
    return len(self.documents)

document_dumps

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

document_dump(file_path)

Placeholder

Source code in swarmauri_base/vector_stores/VectorStoreBase.py

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

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

document_load(file_path)

Placeholder

Source code in swarmauri_base/vector_stores/VectorStoreBase.py

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

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

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

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

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)