[Add] browser-use and main.py

browser-use/browser_use/agent/memory/views.py

@@ -0,0 +1,67 @@
+from typing import Any, Literal
+
+from langchain_core.language_models.chat_models import BaseChatModel
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class MemoryConfig(BaseModel):
+	"""Configuration for procedural memory."""
+
+	model_config = ConfigDict(
+		from_attributes=True, validate_default=True, revalidate_instances='always', validate_assignment=True
+	)
+
+	# Memory settings
+	agent_id: str = Field(default='browser_use_agent', min_length=1)
+	memory_interval: int = Field(default=10, gt=1, lt=100)
+
+	# Embedder settings
+	embedder_provider: Literal['openai', 'gemini', 'ollama', 'huggingface'] = 'huggingface'
+	embedder_model: str = Field(min_length=2, default='all-MiniLM-L6-v2')
+	embedder_dims: int = Field(default=384, gt=10, lt=10000)
+
+	# LLM settings - the LLM instance can be passed separately
+	llm_provider: Literal['langchain'] = 'langchain'
+	llm_instance: BaseChatModel | None = None
+
+	# Vector store settings
+	vector_store_provider: Literal['faiss'] = 'faiss'
+	vector_store_base_path: str = Field(default='/tmp/mem0')
+
+	@property
+	def vector_store_path(self) -> str:
+		"""Returns the full vector store path for the current configuration. e.g. /tmp/mem0_384_faiss"""
+		return f'{self.vector_store_base_path}_{self.embedder_dims}_{self.vector_store_provider}'
+
+	@property
+	def embedder_config_dict(self) -> dict[str, Any]:
+		"""Returns the embedder configuration dictionary."""
+		return {
+			'provider': self.embedder_provider,
+			'config': {'model': self.embedder_model, 'embedding_dims': self.embedder_dims},
+		}
+
+	@property
+	def llm_config_dict(self) -> dict[str, Any]:
+		"""Returns the LLM configuration dictionary."""
+		return {'provider': self.llm_provider, 'config': {'model': self.llm_instance}}
+
+	@property
+	def vector_store_config_dict(self) -> dict[str, Any]:
+		"""Returns the vector store configuration dictionary."""
+		return {
+			'provider': self.vector_store_provider,
+			'config': {
+				'embedding_model_dims': self.embedder_dims,
+				'path': self.vector_store_path,
+			},
+		}
+
+	@property
+	def full_config_dict(self) -> dict[str, dict[str, Any]]:
+		"""Returns the complete configuration dictionary for Mem0."""
+		return {
+			'embedder': self.embedder_config_dict,
+			'llm': self.llm_config_dict,
+			'vector_store': self.vector_store_config_dict,
+		}