memoryscope.scheme
- pydantic model memoryscope.scheme.MemoryNode[源代码]
基类：
BaseModelRepresents a memory node with comprehensive attributes to store memory information including unique ID, user details, content, metadata, scoring metrics. Automatically handles timestamp conversion to date format during initialization.
Show JSON schema
{ "title": "MemoryNode", "description": "Represents a memory node with comprehensive attributes to store memory information including unique ID,\nuser details, content, metadata, scoring metrics.\nAutomatically handles timestamp conversion to date format during initialization.", "type": "object", "properties": { "memory_id": { "description": "unique id for memory", "title": "Memory Id", "type": "string" }, "user_name": { "default": "", "description": "the user who owns the memory", "title": "User Name", "type": "string" }, "target_name": { "default": "", "description": "target name described by the memory", "title": "Target Name", "type": "string" }, "meta_data": { "additionalProperties": { "type": "string" }, "default": {}, "description": "meta data infos", "title": "Meta Data", "type": "object" }, "content": { "default": "", "description": "memory content", "title": "Content", "type": "string" }, "key": { "default": "", "description": "memory key", "title": "Key", "type": "string" }, "key_vector": { "default": [], "description": "memory key embedding result", "items": { "type": "number" }, "title": "Key Vector", "type": "array" }, "value": { "default": "", "description": "memory value", "title": "Value", "type": "string" }, "score_recall": { "default": 0, "description": "embedding similarity score used in recall stage", "title": "Score Recall", "type": "number" }, "score_rank": { "default": 0, "description": "rank model score used in rank stage", "title": "Score Rank", "type": "number" }, "score_rerank": { "default": 0, "description": "rerank score used in rerank stage", "title": "Score Rerank", "type": "number" }, "memory_type": { "default": "", "description": "conversation / observation / insight...", "title": "Memory Type", "type": "string" }, "action_status": { "default": "none", "description": "new / content_modified / modified / deleted / none", "title": "Action Status", "type": "string" }, "store_status": { "default": "valid", "description": "store_status: valid / expired", "title": "Store Status", "type": "string" }, "vector": { "default": [], "description": "content embedding result", "items": { "type": "number" }, "title": "Vector", "type": "array" }, "timestamp": { "description": "timestamp of the memory node", "title": "Timestamp", "type": "integer" }, "dt": { "default": "", "description": "dt of the memory node", "title": "Dt", "type": "string" }, "obs_reflected": { "default": 0, "description": "if the observation is reflected: 0/1", "title": "Obs Reflected", "type": "integer" }, "obs_updated": { "default": 0, "description": "if the observation has updated user profile or insight: 0/1", "title": "Obs Updated", "type": "integer" } } }
- Fields:
- field memory_id: str [Optional]
unique id for memory
- field user_name: str = ''
the user who owns the memory
- field target_name: str = ''
target name described by the memory
- field meta_data: Dict[str, str] = {}
meta data infos
- field content: str = ''
memory content
- field key: str = ''
memory key
- field key_vector: List[float] = []
memory key embedding result
- field value: str = ''
memory value
- field score_recall: float = 0
embedding similarity score used in recall stage
- field score_rank: float = 0
rank model score used in rank stage
- field score_rerank: float = 0
rerank score used in rerank stage
- field memory_type: str = ''
conversation / observation / insight...
- field action_status: str = 'none'
new / content_modified / modified / deleted / none
- field store_status: str = 'valid'
store_status: valid / expired
- field vector: List[float] = []
content embedding result
- field timestamp: int [Optional]
timestamp of the memory node
- field obs_reflected: int = 0
if the observation is reflected: 0/1
- field obs_updated: int = 0
if the observation has updated user profile or insight: 0/1
- __init__(**kwargs)[源代码]
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- field dt: str = ''
dt of the memory node
- property node_keys
- pydantic model memoryscope.scheme.Message[源代码]
基类：
BaseModelRepresents a structured message object with details about the sender, content, and metadata.
- role
The role of the message sender (e.g., 'user', 'assistant', 'system').
- Type:
str
- role_name
Optional name associated with the role of the message sender.
- Type:
str
- content
The actual content or text of the message.
- Type:
str
- time_created
Timestamp indicating when the message was created.
- Type:
int
- memorized
Flag to indicate if the message has been saved or remembered.
- Type:
bool
- meta_data
Additional data or context attached to the message.
- Type:
Dict[str, str]
Show JSON schema
{ "title": "Message", "description": "Represents a structured message object with details about the sender, content, and metadata.\n\nAttributes:\n role (str): The role of the message sender (e.g., 'user', 'assistant', 'system').\n role_name (str): Optional name associated with the role of the message sender.\n content (str): The actual content or text of the message.\n time_created (int): Timestamp indicating when the message was created.\n memorized (bool): Flag to indicate if the message has been saved or remembered.\n meta_data (Dict[str, str]): Additional data or context attached to the message.", "type": "object", "properties": { "role": { "description": "The role of the message sender (user, assistant, system)", "title": "Role", "type": "string" }, "role_name": { "default": "", "description": "Name describing the role of the message sender", "title": "Role Name", "type": "string" }, "content": { "description": "The primary content of the message", "title": "Content", "type": "string" }, "time_created": { "description": "Timestamp marking the message creation time", "title": "Time Created", "type": "integer" }, "memorized": { "default": false, "description": "Indicates if the message is flagged for memory retention", "title": "Memorized", "type": "boolean" }, "meta_data": { "additionalProperties": { "type": "string" }, "default": {}, "description": "Supplementary data attached to the message", "title": "Meta Data", "type": "object" } }, "required": [ "role", "content" ] }
- Fields:
- field role: str [Required]
The role of the message sender (user, assistant, system)
- field role_name: str = ''
Name describing the role of the message sender
- field content: str [Required]
The primary content of the message
- field time_created: int [Optional]
Timestamp marking the message creation time
- field memorized: bool = False
Indicates if the message is flagged for memory retention
- field meta_data: Dict[str, str] = {}
Supplementary data attached to the message
- pydantic model memoryscope.scheme.ModelResponse[源代码]
基类：
BaseModelShow JSON schema
{ "title": "ModelResponse", "type": "object", "properties": { "message": { "anyOf": [ { "$ref": "#/$defs/Message" }, { "type": "null" } ], "default": null, "description": "generation model result" }, "delta": { "default": "", "description": "New text that just streamed in (only used when streaming)", "title": "Delta", "type": "string" }, "embedding_results": { "anyOf": [ { "items": { "items": { "type": "number" }, "type": "array" }, "type": "array" }, { "items": { "type": "number" }, "type": "array" } ], "default": [], "description": "embedding vector", "title": "Embedding Results" }, "rank_scores": { "additionalProperties": { "type": "number" }, "default": {}, "description": "The rank scores of each documents. key: index, value: rank score", "title": "Rank Scores", "type": "object" }, "m_type": { "$ref": "#/$defs/ModelEnum", "default": "generation_model", "description": "One of LLM, EMB, RANK." }, "status": { "default": true, "description": "Indicates whether the model call was successful.", "title": "Status", "type": "boolean" }, "details": { "default": "", "description": "The details information for model call, usually for storage of raw response or failure messages.", "title": "Details", "type": "string" }, "raw": { "default": "", "description": "Raw response from model call", "title": "Raw" }, "meta_data": { "default": {}, "description": "meta data for model response", "title": "Meta Data", "type": "object" } }, "$defs": { "Message": { "description": "Represents a structured message object with details about the sender, content, and metadata.\n\nAttributes:\n role (str): The role of the message sender (e.g., 'user', 'assistant', 'system').\n role_name (str): Optional name associated with the role of the message sender.\n content (str): The actual content or text of the message.\n time_created (int): Timestamp indicating when the message was created.\n memorized (bool): Flag to indicate if the message has been saved or remembered.\n meta_data (Dict[str, str]): Additional data or context attached to the message.", "properties": { "role": { "description": "The role of the message sender (user, assistant, system)", "title": "Role", "type": "string" }, "role_name": { "default": "", "description": "Name describing the role of the message sender", "title": "Role Name", "type": "string" }, "content": { "description": "The primary content of the message", "title": "Content", "type": "string" }, "time_created": { "description": "Timestamp marking the message creation time", "title": "Time Created", "type": "integer" }, "memorized": { "default": false, "description": "Indicates if the message is flagged for memory retention", "title": "Memorized", "type": "boolean" }, "meta_data": { "additionalProperties": { "type": "string" }, "default": {}, "description": "Supplementary data attached to the message", "title": "Meta Data", "type": "object" } }, "required": [ "role", "content" ], "title": "Message", "type": "object" }, "ModelEnum": { "description": "An enumeration representing different types of models used within the system.\n\nMembers:\n GENERATION_MODEL: Represents a model responsible for generating content.\n EMBEDDING_MODEL: Represents a model tasked with creating embeddings, typically used for transforming data into a\n numerical form suitable for machine learning tasks.\n RANK_MODEL: Denotes a model that specializes in ranking, often used to order items based on relevance.", "enum": [ "generation_model", "embedding_model", "rank_model" ], "title": "ModelEnum", "type": "string" } } }
- Fields:
- field delta: str = ''
New text that just streamed in (only used when streaming)
- field embedding_results: List[List[float]] | List[float] = []
embedding vector
- field rank_scores: Dict[int, float] = {}
The rank scores of each documents. key: index, value: rank score
- field status: bool = True
Indicates whether the model call was successful.
- field details: str = ''
The details information for model call, usually for storage of raw response or failure messages.
- field raw: Any = ''
Raw response from model call
- field meta_data: Dict[str, Any] = {}
meta data for model response