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Redis vector store driver

RedisVectorStoreDriver

Bases: BaseVectorStoreDriver

A Vector Store Driver for Redis.

This driver interfaces with a Redis instance and utilizes the Redis hashes and RediSearch module to store, retrieve, and query vectors in a structured manner. Proper setup of the Redis instance and RediSearch is necessary for the driver to function correctly.

Attributes:

Name Type Description
host str

The host of the Redis instance.
port int

The port of the Redis instance.
db int

The database of the Redis instance.
password Optional[str]

The password of the Redis instance.
index str

The name of the index to use.
Source code in griptape/drivers/vector/redis_vector_store_driver.py 
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@define
class RedisVectorStoreDriver(BaseVectorStoreDriver):
    """A Vector Store Driver for Redis.

    This driver interfaces with a Redis instance and utilizes the Redis hashes and RediSearch module to store, retrieve, and query vectors in a structured manner.
    Proper setup of the Redis instance and RediSearch is necessary for the driver to function correctly.

    Attributes:
        host: The host of the Redis instance.
        port: The port of the Redis instance.
        db: The database of the Redis instance.
        password: The password of the Redis instance.
        index: The name of the index to use.
    """

    host: str = field(kw_only=True, metadata={"serializable": True})
    port: int = field(kw_only=True, metadata={"serializable": True})
    db: int = field(kw_only=True, default=0, metadata={"serializable": True})
    password: Optional[str] = field(default=None, kw_only=True, metadata={"serializable": False})
    index: str = field(kw_only=True, metadata={"serializable": True})

    client: Redis = field(
        default=Factory(
            lambda self: import_optional_dependency("redis").Redis(
                host=self.host, port=self.port, db=self.db, password=self.password, decode_responses=False
            ),
            takes_self=True,
        )
    )

    def upsert_vector(
        self,
        vector: list[float],
        vector_id: Optional[str] = None,
        namespace: Optional[str] = None,
        meta: Optional[dict] = None,
        **kwargs,
    ) -> str:
        """Inserts or updates a vector in Redis.

        If a vector with the given vector ID already exists, it is updated; otherwise, a new vector is inserted.
        Metadata associated with the vector can also be provided.
        """
        vector_id = vector_id if vector_id else str_to_hash(str(vector))
        key = self._generate_key(vector_id, namespace)
        bytes_vector = json.dumps(vector).encode("utf-8")

        mapping = {}
        mapping["vector"] = np.array(vector, dtype=np.float32).tobytes()
        mapping["vec_string"] = bytes_vector

        if meta:
            mapping["metadata"] = json.dumps(meta)

        self.client.hset(key, mapping=mapping)

        return vector_id

    def load_entry(self, vector_id: str, namespace: Optional[str] = None) -> Optional[BaseVectorStoreDriver.Entry]:
        """Retrieves a specific vector entry from Redis based on its identifier and optional namespace.

        Returns:
            If the entry is found, it returns an instance of BaseVectorStoreDriver.Entry; otherwise, None is returned.
        """
        key = self._generate_key(vector_id, namespace)
        result = self.client.hgetall(key)
        vector = np.frombuffer(result[b"vector"], dtype=np.float32).tolist()
        meta = json.loads(result[b"metadata"]) if b"metadata" in result else None

        return BaseVectorStoreDriver.Entry(id=vector_id, meta=meta, vector=vector, namespace=namespace)

    def load_entries(self, namespace: Optional[str] = None) -> list[BaseVectorStoreDriver.Entry]:
        """Retrieves all vector entries from Redis that match the optional namespace.

        Returns:
            A list of `BaseVectorStoreDriver.Entry` objects.
        """
        pattern = f"{namespace}:*" if namespace else "*"
        keys = self.client.keys(pattern)

        entries = []
        for key in keys:
            entry = self.load_entry(key.decode("utf-8"), namespace)
            if entry:
                entries.append(entry)

        return entries

    def query(
        self,
        query: str,
        count: Optional[int] = None,
        namespace: Optional[str] = None,
        include_vectors: bool = False,
        **kwargs,
    ) -> list[BaseVectorStoreDriver.QueryResult]:
        """Performs a nearest neighbor search on Redis to find vectors similar to the provided input vector.

        Results can be limited using the count parameter and optionally filtered by a namespace.

        Returns:
            A list of BaseVectorStoreDriver.QueryResult objects, each encapsulating the retrieved vector, its similarity score, metadata, and namespace.
        """
        Query = import_optional_dependency("redis.commands.search.query").Query

        vector = self.embedding_driver.embed_string(query)

        query_expression = (
            Query(f"*=>[KNN {count or 10} @vector $vector as score]")
            .sort_by("score")
            .return_fields("id", "score", "metadata", "vec_string")
            .paging(0, count or 10)
            .dialect(2)
        )

        query_params = {"vector": np.array(vector, dtype=np.float32).tobytes()}

        results = self.client.ft(self.index).search(query_expression, query_params).docs  # pyright: ignore

        query_results = []
        for document in results:
            metadata = getattr(document, "metadata", None)
            namespace = document.id.split(":")[0] if ":" in document.id else None
            vector_id = document.id.split(":")[1] if ":" in document.id else document.id
            vector_float_list = json.loads(document["vec_string"]) if include_vectors else None
            query_results.append(
                BaseVectorStoreDriver.QueryResult(
                    id=vector_id,
                    vector=vector_float_list,
                    score=float(document["score"]),
                    meta=metadata,
                    namespace=namespace,
                )
            )
        return query_results

    def _generate_key(self, vector_id: str, namespace: Optional[str] = None) -> str:
        """Generates a Redis key using the provided vector ID and optionally a namespace."""
        return f"{namespace}:{vector_id}" if namespace else vector_id

    def _get_doc_prefix(self, namespace: Optional[str] = None) -> str:
        """Get the document prefix based on the provided namespace."""
        return f"{namespace}:" if namespace else ""

    def delete_vector(self, vector_id: str):
        raise NotImplementedError(f"{self.__class__.__name__} does not support deletion.")

client: Redis = field(default=Factory(lambda self: import_optional_dependency('redis').Redis(host=self.host, port=self.port, db=self.db, password=self.password, decode_responses=False), takes_self=True)) class-attribute instance-attribute

db: int = field(kw_only=True, default=0, metadata={'serializable': True}) class-attribute instance-attribute

host: str = field(kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

index: str = field(kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

password: Optional[str] = field(default=None, kw_only=True, metadata={'serializable': False}) class-attribute instance-attribute

port: int = field(kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

delete_vector(vector_id)

Source code in griptape/drivers/vector/redis_vector_store_driver.py 
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def delete_vector(self, vector_id: str):
    raise NotImplementedError(f"{self.__class__.__name__} does not support deletion.")

load_entries(namespace=None)

Retrieves all vector entries from Redis that match the optional namespace.

Returns:

Type Description
list[Entry]

A list of BaseVectorStoreDriver.Entry objects.
Source code in griptape/drivers/vector/redis_vector_store_driver.py 
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def load_entries(self, namespace: Optional[str] = None) -> list[BaseVectorStoreDriver.Entry]:
    """Retrieves all vector entries from Redis that match the optional namespace.

    Returns:
        A list of `BaseVectorStoreDriver.Entry` objects.
    """
    pattern = f"{namespace}:*" if namespace else "*"
    keys = self.client.keys(pattern)

    entries = []
    for key in keys:
        entry = self.load_entry(key.decode("utf-8"), namespace)
        if entry:
            entries.append(entry)

    return entries

load_entry(vector_id, namespace=None)

Retrieves a specific vector entry from Redis based on its identifier and optional namespace.

Returns:

Type Description
Optional[Entry]

If the entry is found, it returns an instance of BaseVectorStoreDriver.Entry; otherwise, None is returned.
Source code in griptape/drivers/vector/redis_vector_store_driver.py 
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def load_entry(self, vector_id: str, namespace: Optional[str] = None) -> Optional[BaseVectorStoreDriver.Entry]:
    """Retrieves a specific vector entry from Redis based on its identifier and optional namespace.

    Returns:
        If the entry is found, it returns an instance of BaseVectorStoreDriver.Entry; otherwise, None is returned.
    """
    key = self._generate_key(vector_id, namespace)
    result = self.client.hgetall(key)
    vector = np.frombuffer(result[b"vector"], dtype=np.float32).tolist()
    meta = json.loads(result[b"metadata"]) if b"metadata" in result else None

    return BaseVectorStoreDriver.Entry(id=vector_id, meta=meta, vector=vector, namespace=namespace)

query(query, count=None, namespace=None, include_vectors=False, **kwargs)

Performs a nearest neighbor search on Redis to find vectors similar to the provided input vector.

Results can be limited using the count parameter and optionally filtered by a namespace.

Returns:

Type Description
list[QueryResult]

A list of BaseVectorStoreDriver.QueryResult objects, each encapsulating the retrieved vector, its similarity score, metadata, and namespace.
Source code in griptape/drivers/vector/redis_vector_store_driver.py 
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def query(
    self,
    query: str,
    count: Optional[int] = None,
    namespace: Optional[str] = None,
    include_vectors: bool = False,
    **kwargs,
) -> list[BaseVectorStoreDriver.QueryResult]:
    """Performs a nearest neighbor search on Redis to find vectors similar to the provided input vector.

    Results can be limited using the count parameter and optionally filtered by a namespace.

    Returns:
        A list of BaseVectorStoreDriver.QueryResult objects, each encapsulating the retrieved vector, its similarity score, metadata, and namespace.
    """
    Query = import_optional_dependency("redis.commands.search.query").Query

    vector = self.embedding_driver.embed_string(query)

    query_expression = (
        Query(f"*=>[KNN {count or 10} @vector $vector as score]")
        .sort_by("score")
        .return_fields("id", "score", "metadata", "vec_string")
        .paging(0, count or 10)
        .dialect(2)
    )

    query_params = {"vector": np.array(vector, dtype=np.float32).tobytes()}

    results = self.client.ft(self.index).search(query_expression, query_params).docs  # pyright: ignore

    query_results = []
    for document in results:
        metadata = getattr(document, "metadata", None)
        namespace = document.id.split(":")[0] if ":" in document.id else None
        vector_id = document.id.split(":")[1] if ":" in document.id else document.id
        vector_float_list = json.loads(document["vec_string"]) if include_vectors else None
        query_results.append(
            BaseVectorStoreDriver.QueryResult(
                id=vector_id,
                vector=vector_float_list,
                score=float(document["score"]),
                meta=metadata,
                namespace=namespace,
            )
        )
    return query_results

upsert_vector(vector, vector_id=None, namespace=None, meta=None, **kwargs)

Inserts or updates a vector in Redis.

If a vector with the given vector ID already exists, it is updated; otherwise, a new vector is inserted. Metadata associated with the vector can also be provided.

Source code in griptape/drivers/vector/redis_vector_store_driver.py 
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def upsert_vector(
    self,
    vector: list[float],
    vector_id: Optional[str] = None,
    namespace: Optional[str] = None,
    meta: Optional[dict] = None,
    **kwargs,
) -> str:
    """Inserts or updates a vector in Redis.

    If a vector with the given vector ID already exists, it is updated; otherwise, a new vector is inserted.
    Metadata associated with the vector can also be provided.
    """
    vector_id = vector_id if vector_id else str_to_hash(str(vector))
    key = self._generate_key(vector_id, namespace)
    bytes_vector = json.dumps(vector).encode("utf-8")

    mapping = {}
    mapping["vector"] = np.array(vector, dtype=np.float32).tobytes()
    mapping["vec_string"] = bytes_vector

    if meta:
        mapping["metadata"] = json.dumps(meta)

    self.client.hset(key, mapping=mapping)

    return vector_id