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135 lines
7.7 KiB
Python
135 lines
7.7 KiB
Python
from typing import List, Optional, Dict, Literal, Type
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from pydantic import BaseModel, Field, Json, ConfigDict
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import uuid
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import base64
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import numpy as np
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from datetime import datetime
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from sqlmodel import Field, SQLModel
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from sqlalchemy import JSON, Column, BINARY, TypeDecorator
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from memgpt.constants import DEFAULT_HUMAN, DEFAULT_MEMGPT_MODEL, DEFAULT_PERSONA, DEFAULT_PRESET, LLM_MAX_TOKENS, MAX_EMBEDDING_DIM
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from memgpt.utils import get_human_text, get_persona_text, printd, get_utc_time
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class LLMConfigModel(BaseModel):
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model: Optional[str] = "gpt-4"
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model_endpoint_type: Optional[str] = "openai"
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model_endpoint: Optional[str] = "https://api.openai.com/v1"
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model_wrapper: Optional[str] = None
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context_window: Optional[int] = None
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# FIXME hack to silence pydantic protected namespace warning
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model_config = ConfigDict(protected_namespaces=())
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class EmbeddingConfigModel(BaseModel):
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embedding_endpoint_type: Optional[str] = "openai"
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embedding_endpoint: Optional[str] = "https://api.openai.com/v1"
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embedding_model: Optional[str] = "text-embedding-ada-002"
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embedding_dim: Optional[int] = 1536
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embedding_chunk_size: Optional[int] = 300
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class PresetModel(BaseModel):
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name: str = Field(..., description="The name of the preset.")
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id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the preset.")
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user_id: Optional[uuid.UUID] = Field(None, description="The unique identifier of the user who created the preset.")
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description: Optional[str] = Field(None, description="The description of the preset.")
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created_at: datetime = Field(default_factory=get_utc_time, description="The unix timestamp of when the preset was created.")
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system: str = Field(..., description="The system prompt of the preset.")
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persona: str = Field(default=get_persona_text(DEFAULT_PERSONA), description="The persona of the preset.")
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persona_name: Optional[str] = Field(None, description="The name of the persona of the preset.")
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human: str = Field(default=get_human_text(DEFAULT_HUMAN), description="The human of the preset.")
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human_name: Optional[str] = Field(None, description="The name of the human of the preset.")
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functions_schema: List[Dict] = Field(..., description="The functions schema of the preset.")
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class ToolModel(BaseModel):
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# TODO move into database
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name: str = Field(..., description="The name of the function.")
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json_schema: dict = Field(..., description="The JSON schema of the function.")
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tags: List[str] = Field(..., description="Metadata tags.")
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source_type: Optional[Literal["python"]] = Field(None, description="The type of the source code.")
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source_code: Optional[str] = Field(..., description="The source code of the function.")
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class AgentStateModel(BaseModel):
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id: uuid.UUID = Field(..., description="The unique identifier of the agent.")
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name: str = Field(..., description="The name of the agent.")
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description: Optional[str] = Field(None, description="The description of the agent.")
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user_id: uuid.UUID = Field(..., description="The unique identifier of the user associated with the agent.")
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# timestamps
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# created_at: datetime = Field(default_factory=get_utc_time, description="The unix timestamp of when the agent was created.")
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created_at: int = Field(..., description="The unix timestamp of when the agent was created.")
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# preset information
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preset: str = Field(..., description="The preset used by the agent.")
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persona: str = Field(..., description="The persona used by the agent.")
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human: str = Field(..., description="The human used by the agent.")
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functions_schema: List[Dict] = Field(..., description="The functions schema used by the agent.")
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# llm information
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llm_config: LLMConfigModel = Field(..., description="The LLM configuration used by the agent.")
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embedding_config: EmbeddingConfigModel = Field(..., description="The embedding configuration used by the agent.")
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# agent state
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state: Optional[Dict] = Field(None, description="The state of the agent.")
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class CoreMemory(BaseModel):
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human: str = Field(..., description="Human element of the core memory.")
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persona: str = Field(..., description="Persona element of the core memory.")
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class HumanModel(SQLModel, table=True):
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text: str = Field(default=get_human_text(DEFAULT_HUMAN), description="The human text.")
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name: str = Field(..., description="The name of the human.")
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id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the human.", primary_key=True)
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user_id: Optional[uuid.UUID] = Field(..., description="The unique identifier of the user associated with the human.")
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class PersonaModel(SQLModel, table=True):
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text: str = Field(default=get_persona_text(DEFAULT_PERSONA), description="The persona text.")
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name: str = Field(..., description="The name of the persona.")
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id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the persona.", primary_key=True)
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user_id: Optional[uuid.UUID] = Field(..., description="The unique identifier of the user associated with the persona.")
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class SourceModel(SQLModel, table=True):
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name: str = Field(..., description="The name of the source.")
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description: Optional[str] = Field(None, description="The description of the source.")
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user_id: uuid.UUID = Field(..., description="The unique identifier of the user associated with the source.")
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created_at: datetime = Field(default_factory=get_utc_time, description="The unix timestamp of when the source was created.")
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id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the source.", primary_key=True)
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description: Optional[str] = Field(None, description="The description of the source.")
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# embedding info
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# embedding_config: EmbeddingConfigModel = Field(..., description="The embedding configuration used by the source.")
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embedding_config: Optional[EmbeddingConfigModel] = Field(
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None, sa_column=Column(JSON), description="The embedding configuration used by the passage."
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)
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# NOTE: .metadata is a reserved attribute on SQLModel
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metadata_: Optional[dict] = Field(None, sa_column=Column(JSON), description="Metadata associated with the source.")
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class PassageModel(BaseModel):
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user_id: Optional[uuid.UUID] = Field(None, description="The unique identifier of the user associated with the passage.")
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agent_id: Optional[uuid.UUID] = Field(None, description="The unique identifier of the agent associated with the passage.")
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text: str = Field(..., description="The text of the passage.")
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embedding: Optional[List[float]] = Field(None, description="The embedding of the passage.")
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embedding_config: Optional[EmbeddingConfigModel] = Field(
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None, sa_column=Column(JSON), description="The embedding configuration used by the passage."
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)
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data_source: Optional[str] = Field(None, description="The data source of the passage.")
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doc_id: Optional[uuid.UUID] = Field(None, description="The unique identifier of the document associated with the passage.")
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id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the passage.", primary_key=True)
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metadata: Optional[Dict] = Field({}, description="The metadata of the passage.")
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class DocumentModel(BaseModel):
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user_id: uuid.UUID = Field(..., description="The unique identifier of the user associated with the document.")
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text: str = Field(..., description="The text of the document.")
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data_source: str = Field(..., description="The data source of the document.")
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id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the document.", primary_key=True)
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metadata: Optional[Dict] = Field({}, description="The metadata of the document.")
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