mirror of
https://github.com/browser-use/browser-use
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68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from typing import Any, Literal
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from langchain_core.language_models.chat_models import BaseChatModel
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from pydantic import BaseModel, ConfigDict, Field
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class MemoryConfig(BaseModel):
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"""Configuration for procedural memory."""
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model_config = ConfigDict(
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from_attributes=True, validate_default=True, revalidate_instances='always', validate_assignment=True
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)
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# Memory settings
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agent_id: str = Field(default='browser_use_agent', min_length=1)
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memory_interval: int = Field(default=10, gt=1, lt=100)
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# Embedder settings
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embedder_provider: Literal['openai', 'gemini', 'ollama', 'huggingface'] = 'huggingface'
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embedder_model: str = Field(min_length=2, default='all-MiniLM-L6-v2')
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embedder_dims: int = Field(default=384, gt=10, lt=10000)
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# LLM settings - the LLM instance can be passed separately
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llm_provider: Literal['langchain'] = 'langchain'
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llm_instance: BaseChatModel | None = None
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# Vector store settings
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vector_store_provider: Literal['faiss'] = 'faiss'
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vector_store_base_path: str = Field(default='/tmp/mem0')
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@property
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def vector_store_path(self) -> str:
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"""Returns the full vector store path for the current configuration. e.g. /tmp/mem0_384_faiss"""
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return f'{self.vector_store_base_path}_{self.embedder_dims}_{self.vector_store_provider}'
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@property
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def embedder_config_dict(self) -> dict[str, Any]:
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"""Returns the embedder configuration dictionary."""
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return {
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'provider': self.embedder_provider,
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'config': {'model': self.embedder_model, 'embedding_dims': self.embedder_dims},
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}
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@property
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def llm_config_dict(self) -> dict[str, Any]:
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"""Returns the LLM configuration dictionary."""
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return {'provider': self.llm_provider, 'config': {'model': self.llm_instance}}
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@property
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def vector_store_config_dict(self) -> dict[str, Any]:
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"""Returns the vector store configuration dictionary."""
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return {
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'provider': self.vector_store_provider,
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'config': {
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'embedding_model_dims': self.embedder_dims,
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'path': self.vector_store_path,
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},
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}
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@property
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def full_config_dict(self) -> dict[str, dict[str, Any]]:
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"""Returns the complete configuration dictionary for Mem0."""
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return {
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'embedder': self.embedder_config_dict,
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'llm': self.llm_config_dict,
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'vector_store': self.vector_store_config_dict,
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}
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