camel.storages.vectordb_storages package

Contents

camel.storages.vectordb_storages package#

Submodules#

camel.storages.vectordb_storages.base module#

class camel.storages.vectordb_storages.base.BaseVectorStorage[source]#

Bases: ABC

An abstract base class for vector storage systems.

abstract add(records: List[VectorRecord], **kwargs: Any) → None[source]#

Saves a list of vector records to the storage.

Parameters:

  • records (List[VectorRecord]) – List of vector records to be saved.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there is an error during the saving process.

abstract clear() → None[source]#

Remove all vectors from the storage.

abstract property client: Any#

Provides access to the underlying vector database client.

abstract delete(ids: List[str], **kwargs: Any) → None[source]#

Deletes a list of vectors identified by their IDs from the storage.

Parameters:

  • ids (List[str]) – List of unique identifiers for the vectors to be deleted.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there is an error during the deletion process.

get_payloads_by_vector(vector: List[float], top_k: int) → List[Dict[str, Any]][source]#

Returns payloads of top k vector records that closest to the given vector.

This function is a wrapper of BaseVectorStorage.query.

Parameters:

  • vector (List[float]) – The search vector.

  • top_k (int) – The number of top similer vectors.

Returns:

A list of vector payloads retrieved

from the storage based on similarity to the query vector.

Return type:

List[List[Dict[str, Any]]]

abstract load() → None[source]#

Load the collection hosted on cloud service.

abstract query(query: VectorDBQuery, **kwargs: Any) → List[VectorDBQueryResult][source]#

Searches for similar vectors in the storage based on the provided query.

Parameters:

  • query (VectorDBQuery) – The query object containing the search vector and the number of top similar vectors to retrieve.

  • **kwargs (Any) – Additional keyword arguments.

Returns:

A list of vectors retrieved from the

storage based on similarity to the query vector.

Return type:

List[VectorDBQueryResult]

abstract status() → VectorDBStatus[source]#

Returns status of the vector database.

Returns:

The vector database status.

Return type:

VectorDBStatus

class camel.storages.vectordb_storages.base.VectorDBQuery(query_vector: List[float], top_k: int)[source]#

Bases: BaseModel

Represents a query to a vector database.

query_vector#

The numerical representation of the query vector.

Type:

List[float]

top_k#

The number of top similar vectors to retrieve from the database. (default: 1)

Type:

int, optional

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'query_vector': FieldInfo(annotation=List[float], required=True), 'top_k': FieldInfo(annotation=int, required=False, default=1)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

query_vector: List[float]#

The numerical representation of the query vector.

top_k: int#

The number of top similar vectors to retrieve from the database.

class camel.storages.vectordb_storages.base.VectorDBQueryResult(*, record: VectorRecord, similarity: float)[source]#

Bases: BaseModel

Encapsulates the result of a query against a vector database.

record#

The target vector record.

Type:

VectorRecord

similarity#

The similarity score between the query vector and the record.

Type:

float

classmethod create(similarity: float, vector: List[float], id: str, payload: Dict[str, Any] | None = None) → VectorDBQueryResult[source]#

A class method to construct a VectorDBQueryResult instance.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'record': FieldInfo(annotation=VectorRecord, required=True), 'similarity': FieldInfo(annotation=float, required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

record: VectorRecord#
similarity: float#
class camel.storages.vectordb_storages.base.VectorDBStatus(*, vector_dim: int, vector_count: int)[source]#

Bases: BaseModel

Vector database status.

vector_dim#

The dimention of stored vectors.

Type:

int

vector_count#

The number of stored vectors.

Type:

int

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'vector_count': FieldInfo(annotation=int, required=True), 'vector_dim': FieldInfo(annotation=int, required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

vector_count: int#
vector_dim: int#
class camel.storages.vectordb_storages.base.VectorRecord(*, vector: List[float], id: str = None, payload: Dict[str, Any] | None = None)[source]#

Bases: BaseModel

Encapsulates information about a vector’s unique identifier and its payload, which is primarily used as a data transfer object when saving to vector storage.

vector#

The numerical representation of the vector.

Type:

List[float]

id#

A unique identifier for the vector. If not provided, an random uuid will be assigned.

Type:

str, optional

payload#

Any additional metadata or information related to the vector. (default: None)

Type:

Optional[Dict[str, Any]], optional

id: str#
model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'id': FieldInfo(annotation=str, required=False, default_factory=<lambda>), 'payload': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, default=None), 'vector': FieldInfo(annotation=List[float], required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

payload: Dict[str, Any] | None#
vector: List[float]#

camel.storages.vectordb_storages.milvus module#

class camel.storages.vectordb_storages.milvus.MilvusStorage(vector_dim: int, url_and_api_key: Tuple[str, str], collection_name: str | None = None, **kwargs: Any)[source]#

Bases: BaseVectorStorage

An implementation of the BaseVectorStorage for interacting with Milvus, a cloud-native vector search engine.

The detailed information about Milvus is available at: Milvus

Parameters:

  • vector_dim (int) – The dimenstion of storing vectors.

  • url_and_api_key (Tuple[str, str]) – Tuple containing the URL and API key for connecting to a remote Milvus instance. URL maps to Milvus uri concept, typically “endpoint:port”. API key maps to Milvus token concept, for self-hosted it’s “username:pwd”, for Zilliz Cloud (fully-managed Milvus) it’s API Key.

  • collection_name (Optional[str], optional) – Name for the collection in the Milvus. If not provided, set it to the current time with iso format. (default: None)

  • **kwargs (Any) – Additional keyword arguments for initializing MilvusClient.

Raises:

ImportError – If pymilvus package is not installed.

add(records: List[VectorRecord], **kwargs) → None[source]#

Adds a list of vectors to the specified collection.

Parameters:

  • records (List[VectorRecord]) – List of vectors to be added.

  • **kwargs (Any) – Additional keyword arguments pass to insert.

Raises:

RuntimeError – If there was an error in the addition process.

clear() → None[source]#

Removes all vectors from the Milvus collection. This method deletes the existing collection and then recreates it with the same schema to effectively remove all stored vectors.

property client: Any#

Provides direct access to the Milvus client. This property allows for direct interactions with the Milvus client for operations that are not covered by the MilvusStorage class.

Returns:

The Milvus client instance.

Return type:

Any

delete(ids: List[str], **kwargs: Any) → None[source]#

Deletes a list of vectors identified by their IDs from the storage. If unsure of ids you can first query the collection to grab the corresponding data.

Parameters:

  • ids (List[str]) – List of unique identifiers for the vectors to be deleted.

  • **kwargs (Any) – Additional keyword arguments passed to delete.

Raises:

RuntimeError – If there is an error during the deletion process.

load() → None[source]#

Load the collection hosted on cloud service.

query(query: VectorDBQuery, **kwargs: Any) → List[VectorDBQueryResult][source]#

Searches for similar vectors in the storage based on the provided query.

Parameters:

  • query (VectorDBQuery) – The query object containing the search vector and the number of top similar vectors to retrieve.

  • **kwargs (Any) – Additional keyword arguments passed to search.

Returns:

A list of vectors retrieved from the

storage based on similarity to the query vector.

Return type:

List[VectorDBQueryResult]

status() → VectorDBStatus[source]#

Retrieves the current status of the Milvus collection. This method provides information about the collection, including its vector dimensionality and the total number of vectors stored.

Returns:

An object containing information about the

collection’s status.

Return type:

VectorDBStatus

camel.storages.vectordb_storages.qdrant module#

class camel.storages.vectordb_storages.qdrant.QdrantStorage(vector_dim: int, collection_name: str | None = None, url_and_api_key: Tuple[str, str] | None = None, path: str | None = None, distance: VectorDistance = VectorDistance.COSINE, delete_collection_on_del: bool = False, **kwargs: Any)[source]#

Bases: BaseVectorStorage

An implementation of the BaseVectorStorage for interacting with Qdrant, a vector search engine.

The detailed information about Qdrant is available at: Qdrant

Parameters:

  • vector_dim (int) – The dimenstion of storing vectors.

  • collection_name (Optional[str], optional) – Name for the collection in the Qdrant. If not provided, set it to the current time with iso format. (default: None)

  • url_and_api_key (Optional[Tuple[str, str]], optional) – Tuple containing the URL and API key for connecting to a remote Qdrant instance. (default: None)

  • path (Optional[str], optional) – Path to a directory for initializing a local Qdrant client. (default: None)

  • distance (VectorDistance, optional) – The distance metric for vector comparison (default: VectorDistance.COSINE)

  • delete_collection_on_del (bool, optional) – Flag to determine if the collection should be deleted upon object destruction. (default: False)

  • **kwargs (Any) – Additional keyword arguments for initializing QdrantClient.

Notes

  • If url_and_api_key is provided, it takes priority and the client will attempt to connect to the remote Qdrant instance using the URL endpoint.

  • If url_and_api_key is not provided and path is given, the client will use the local path to initialize Qdrant.

  • If neither url_and_api_key nor path is provided, the client will be initialized with an in-memory storage (“:memory:”).

add(records: List[VectorRecord], **kwargs) → None[source]#

Adds a list of vectors to the specified collection.

Parameters:

  • vectors (List[VectorRecord]) – List of vectors to be added.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there was an error in the addition process.

clear() → None[source]#

Remove all vectors from the storage.

property client: QdrantClient#

Provides access to the underlying vector database client.

close_client(**kwargs)[source]#

Closes the client connection to the Qdrant storage.

delete(ids: List[str] | None = None, payload_filter: Dict[str, Any] | None = None, **kwargs: Any) → None[source]#

Deletes points from the collection based on either IDs or payload filters.

Parameters:

  • ids (Optional[List[str]], optional) – List of unique identifiers for the vectors to be deleted.

  • payload_filter (Optional[Dict[str, Any]], optional) – A filter for the payload to delete points matching specific conditions. If ids is provided, payload_filter will be ignored unless both are combined explicitly.

  • **kwargs (Any) – Additional keyword arguments pass to QdrantClient. delete.

Examples

>>> # Delete points with IDs "1", "2", and "3"
>>> storage.delete(ids=["1", "2", "3"])
>>> # Delete points with payload filter
>>> storage.delete(payload_filter={"name": "Alice"})
Raises:

  • ValueError – If neither ids nor payload_filter is provided.

  • RuntimeError – If there is an error during the deletion process.

Notes

  • If ids is provided, the points with these IDs will be deleted

    directly, and the payload_filter will be ignored.

  • If ids is not provided but payload_filter is, then points

    matching the payload_filter will be deleted.

delete_collection() → None[source]#

Deletes the entire collection in the Qdrant storage.

load() → None[source]#

Load the collection hosted on cloud service.

query(query: VectorDBQuery, filter_conditions: Dict[str, Any] | None = None, **kwargs: Any) → List[VectorDBQueryResult][source]#

Searches for similar vectors in the storage based on the provided query.

Parameters:

  • query (VectorDBQuery) – The query object containing the search vector and the number of top similar vectors to retrieve.

  • filter_conditions (Optional[Dict[str, Any]], optional) – A dictionary specifying conditions to filter the query results.

  • **kwargs (Any) – Additional keyword arguments.

Returns:

A list of vectors retrieved from the

storage based on similarity to the query vector.

Return type:

List[VectorDBQueryResult]

status() → VectorDBStatus[source]#

Returns status of the vector database.

Returns:

The vector database status.

Return type:

VectorDBStatus

update_payload(ids: List[str], payload: Dict[str, Any], **kwargs: Any) → None[source]#

Updates the payload of the vectors identified by their IDs.

Parameters:

  • ids (List[str]) – List of unique identifiers for the vectors to be updated.

  • payload (Dict[str, Any]) – List of payloads to be updated.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there is an error during the update process.

Module contents#

class camel.storages.vectordb_storages.BaseVectorStorage[source]#

Bases: ABC

An abstract base class for vector storage systems.

abstract add(records: List[VectorRecord], **kwargs: Any) → None[source]#

Saves a list of vector records to the storage.

Parameters:

  • records (List[VectorRecord]) – List of vector records to be saved.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there is an error during the saving process.

abstract clear() → None[source]#

Remove all vectors from the storage.

abstract property client: Any#

Provides access to the underlying vector database client.

abstract delete(ids: List[str], **kwargs: Any) → None[source]#

Deletes a list of vectors identified by their IDs from the storage.

Parameters:

  • ids (List[str]) – List of unique identifiers for the vectors to be deleted.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there is an error during the deletion process.

get_payloads_by_vector(vector: List[float], top_k: int) → List[Dict[str, Any]][source]#

Returns payloads of top k vector records that closest to the given vector.

This function is a wrapper of BaseVectorStorage.query.

Parameters:

  • vector (List[float]) – The search vector.

  • top_k (int) – The number of top similer vectors.

Returns:

A list of vector payloads retrieved

from the storage based on similarity to the query vector.

Return type:

List[List[Dict[str, Any]]]

abstract load() → None[source]#

Load the collection hosted on cloud service.

abstract query(query: VectorDBQuery, **kwargs: Any) → List[VectorDBQueryResult][source]#

Searches for similar vectors in the storage based on the provided query.

Parameters:

  • query (VectorDBQuery) – The query object containing the search vector and the number of top similar vectors to retrieve.

  • **kwargs (Any) – Additional keyword arguments.

Returns:

A list of vectors retrieved from the

storage based on similarity to the query vector.

Return type:

List[VectorDBQueryResult]

abstract status() → VectorDBStatus[source]#

Returns status of the vector database.

Returns:

The vector database status.

Return type:

VectorDBStatus

class camel.storages.vectordb_storages.MilvusStorage(vector_dim: int, url_and_api_key: Tuple[str, str], collection_name: str | None = None, **kwargs: Any)[source]#

Bases: BaseVectorStorage

An implementation of the BaseVectorStorage for interacting with Milvus, a cloud-native vector search engine.

The detailed information about Milvus is available at: Milvus

Parameters:

  • vector_dim (int) – The dimenstion of storing vectors.

  • url_and_api_key (Tuple[str, str]) – Tuple containing the URL and API key for connecting to a remote Milvus instance. URL maps to Milvus uri concept, typically “endpoint:port”. API key maps to Milvus token concept, for self-hosted it’s “username:pwd”, for Zilliz Cloud (fully-managed Milvus) it’s API Key.

  • collection_name (Optional[str], optional) – Name for the collection in the Milvus. If not provided, set it to the current time with iso format. (default: None)

  • **kwargs (Any) – Additional keyword arguments for initializing MilvusClient.

Raises:

ImportError – If pymilvus package is not installed.

add(records: List[VectorRecord], **kwargs) → None[source]#

Adds a list of vectors to the specified collection.

Parameters:

  • records (List[VectorRecord]) – List of vectors to be added.

  • **kwargs (Any) – Additional keyword arguments pass to insert.

Raises:

RuntimeError – If there was an error in the addition process.

clear() → None[source]#

Removes all vectors from the Milvus collection. This method deletes the existing collection and then recreates it with the same schema to effectively remove all stored vectors.

property client: Any#

Provides direct access to the Milvus client. This property allows for direct interactions with the Milvus client for operations that are not covered by the MilvusStorage class.

Returns:

The Milvus client instance.

Return type:

Any

delete(ids: List[str], **kwargs: Any) → None[source]#

Deletes a list of vectors identified by their IDs from the storage. If unsure of ids you can first query the collection to grab the corresponding data.

Parameters:

  • ids (List[str]) – List of unique identifiers for the vectors to be deleted.

  • **kwargs (Any) – Additional keyword arguments passed to delete.

Raises:

RuntimeError – If there is an error during the deletion process.

load() → None[source]#

Load the collection hosted on cloud service.

query(query: VectorDBQuery, **kwargs: Any) → List[VectorDBQueryResult][source]#

Searches for similar vectors in the storage based on the provided query.

Parameters:

  • query (VectorDBQuery) – The query object containing the search vector and the number of top similar vectors to retrieve.

  • **kwargs (Any) – Additional keyword arguments passed to search.

Returns:

A list of vectors retrieved from the

storage based on similarity to the query vector.

Return type:

List[VectorDBQueryResult]

status() → VectorDBStatus[source]#

Retrieves the current status of the Milvus collection. This method provides information about the collection, including its vector dimensionality and the total number of vectors stored.

Returns:

An object containing information about the

collection’s status.

Return type:

VectorDBStatus

class camel.storages.vectordb_storages.QdrantStorage(vector_dim: int, collection_name: str | None = None, url_and_api_key: Tuple[str, str] | None = None, path: str | None = None, distance: VectorDistance = VectorDistance.COSINE, delete_collection_on_del: bool = False, **kwargs: Any)[source]#

Bases: BaseVectorStorage

An implementation of the BaseVectorStorage for interacting with Qdrant, a vector search engine.

The detailed information about Qdrant is available at: Qdrant

Parameters:

  • vector_dim (int) – The dimenstion of storing vectors.

  • collection_name (Optional[str], optional) – Name for the collection in the Qdrant. If not provided, set it to the current time with iso format. (default: None)

  • url_and_api_key (Optional[Tuple[str, str]], optional) – Tuple containing the URL and API key for connecting to a remote Qdrant instance. (default: None)

  • path (Optional[str], optional) – Path to a directory for initializing a local Qdrant client. (default: None)

  • distance (VectorDistance, optional) – The distance metric for vector comparison (default: VectorDistance.COSINE)

  • delete_collection_on_del (bool, optional) – Flag to determine if the collection should be deleted upon object destruction. (default: False)

  • **kwargs (Any) – Additional keyword arguments for initializing QdrantClient.

Notes

  • If url_and_api_key is provided, it takes priority and the client will attempt to connect to the remote Qdrant instance using the URL endpoint.

  • If url_and_api_key is not provided and path is given, the client will use the local path to initialize Qdrant.

  • If neither url_and_api_key nor path is provided, the client will be initialized with an in-memory storage (“:memory:”).

add(records: List[VectorRecord], **kwargs) → None[source]#

Adds a list of vectors to the specified collection.

Parameters:

  • vectors (List[VectorRecord]) – List of vectors to be added.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there was an error in the addition process.

clear() → None[source]#

Remove all vectors from the storage.

property client: QdrantClient#

Provides access to the underlying vector database client.

close_client(**kwargs)[source]#

Closes the client connection to the Qdrant storage.

delete(ids: List[str] | None = None, payload_filter: Dict[str, Any] | None = None, **kwargs: Any) → None[source]#

Deletes points from the collection based on either IDs or payload filters.

Parameters:

  • ids (Optional[List[str]], optional) – List of unique identifiers for the vectors to be deleted.

  • payload_filter (Optional[Dict[str, Any]], optional) – A filter for the payload to delete points matching specific conditions. If ids is provided, payload_filter will be ignored unless both are combined explicitly.

  • **kwargs (Any) – Additional keyword arguments pass to QdrantClient. delete.

Examples

>>> # Delete points with IDs "1", "2", and "3"
>>> storage.delete(ids=["1", "2", "3"])
>>> # Delete points with payload filter
>>> storage.delete(payload_filter={"name": "Alice"})
Raises:

  • ValueError – If neither ids nor payload_filter is provided.

  • RuntimeError – If there is an error during the deletion process.

Notes

  • If ids is provided, the points with these IDs will be deleted

    directly, and the payload_filter will be ignored.

  • If ids is not provided but payload_filter is, then points

    matching the payload_filter will be deleted.

delete_collection() → None[source]#

Deletes the entire collection in the Qdrant storage.

load() → None[source]#

Load the collection hosted on cloud service.

query(query: VectorDBQuery, filter_conditions: Dict[str, Any] | None = None, **kwargs: Any) → List[VectorDBQueryResult][source]#

Searches for similar vectors in the storage based on the provided query.

Parameters:

  • query (VectorDBQuery) – The query object containing the search vector and the number of top similar vectors to retrieve.

  • filter_conditions (Optional[Dict[str, Any]], optional) – A dictionary specifying conditions to filter the query results.

  • **kwargs (Any) – Additional keyword arguments.

Returns:

A list of vectors retrieved from the

storage based on similarity to the query vector.

Return type:

List[VectorDBQueryResult]

status() → VectorDBStatus[source]#

Returns status of the vector database.

Returns:

The vector database status.

Return type:

VectorDBStatus

update_payload(ids: List[str], payload: Dict[str, Any], **kwargs: Any) → None[source]#

Updates the payload of the vectors identified by their IDs.

Parameters:

  • ids (List[str]) – List of unique identifiers for the vectors to be updated.

  • payload (Dict[str, Any]) – List of payloads to be updated.

  • **kwargs (Any) – Additional keyword arguments.

Raises:

RuntimeError – If there is an error during the update process.

class camel.storages.vectordb_storages.VectorDBQuery(query_vector: List[float], top_k: int)[source]#

Bases: BaseModel

Represents a query to a vector database.

query_vector#

The numerical representation of the query vector.

Type:

List[float]

top_k#

The number of top similar vectors to retrieve from the database. (default: 1)

Type:

int, optional

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'query_vector': FieldInfo(annotation=List[float], required=True), 'top_k': FieldInfo(annotation=int, required=False, default=1)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

query_vector: List[float]#

The numerical representation of the query vector.

top_k: int#

The number of top similar vectors to retrieve from the database.

class camel.storages.vectordb_storages.VectorDBQueryResult(*, record: VectorRecord, similarity: float)[source]#

Bases: BaseModel

Encapsulates the result of a query against a vector database.

record#

The target vector record.

Type:

VectorRecord

similarity#

The similarity score between the query vector and the record.

Type:

float

classmethod create(similarity: float, vector: List[float], id: str, payload: Dict[str, Any] | None = None) → VectorDBQueryResult[source]#

A class method to construct a VectorDBQueryResult instance.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'record': FieldInfo(annotation=VectorRecord, required=True), 'similarity': FieldInfo(annotation=float, required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

record: VectorRecord#
similarity: float#
class camel.storages.vectordb_storages.VectorDBStatus(*, vector_dim: int, vector_count: int)[source]#

Bases: BaseModel

Vector database status.

vector_dim#

The dimention of stored vectors.

Type:

int

vector_count#

The number of stored vectors.

Type:

int

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'vector_count': FieldInfo(annotation=int, required=True), 'vector_dim': FieldInfo(annotation=int, required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

vector_count: int#
vector_dim: int#
class camel.storages.vectordb_storages.VectorRecord(*, vector: List[float], id: str = None, payload: Dict[str, Any] | None = None)[source]#

Bases: BaseModel

Encapsulates information about a vector’s unique identifier and its payload, which is primarily used as a data transfer object when saving to vector storage.

vector#

The numerical representation of the vector.

Type:

List[float]

id#

A unique identifier for the vector. If not provided, an random uuid will be assigned.

Type:

str, optional

payload#

Any additional metadata or information related to the vector. (default: None)

Type:

Optional[Dict[str, Any]], optional

id: str#
model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}#

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'id': FieldInfo(annotation=str, required=False, default_factory=<lambda>), 'payload': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, default=None), 'vector': FieldInfo(annotation=List[float], required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

payload: Dict[str, Any] | None#
vector: List[float]#
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