ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter3/dense-embedding/config.py

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+"""Configuration for the dense embedding service."""
+
+import os
+from enum import Enum
+from dataclasses import dataclass, field
+from typing import Optional
+
+class IndexType(Enum):
+    """Supported index types."""
+    ANNOY = "annoy"
+    HNSW = "hnsw"
+
+@dataclass
+class ServiceConfig:
+    """Service configuration."""
+    # Server settings
+    host: str = "0.0.0.0"
+    port: int = 4240  # Default port for dense embedding service
+    
+    # Model settings
+    model_name: str = "BAAI/bge-m3"
+    use_fp16: bool = True
+    max_seq_length: int = 8192  # Increased to match HARD_LIMIT in chunking
+    
+    # Index settings
+    index_type: IndexType = IndexType.HNSW
+    max_documents: int = 100000
+    
+    # HNSW specific settings
+    hnsw_ef_construction: int = 200
+    hnsw_M: int = 16
+    hnsw_ef_search: int = 50
+    hnsw_space: str = "cosine"
+    
+    # Annoy specific settings
+    annoy_n_trees: int = 50
+    annoy_metric: str = "angular"
+    
+    # Logging settings
+    log_level: str = "INFO"
+    debug: bool = False
+    show_embeddings: bool = False
+    
+    @classmethod
+    def from_env(cls):
+        """Create config from environment variables."""
+        config = cls()
+        
+        # Override with environment variables if present
+        if os.getenv("DENSE_PORT"):
+            config.port = int(os.getenv("DENSE_PORT"))
+        if os.getenv("DENSE_HOST"):
+            config.host = os.getenv("DENSE_HOST")
+        if os.getenv("DENSE_MODEL"):
+            config.model_name = os.getenv("DENSE_MODEL")
+        if os.getenv("DEBUG"):
+            config.debug = os.getenv("DEBUG").lower() == "true"
+            
+        return config