Create config.py

config.py

@@ -0,0 +1,62 @@
+"""
+Configuration file for RS Studies MCP Server
+Customize these settings based on your local setup.
+"""
+
+import os
+import torch
+
+class Config:
+    """Configuration settings for the RS Studies MCP Server"""
+    
+    # Server Information
+    SERVER_NAME = "rs-studies-mcp"
+    SERVER_DESCRIPTION = "RS Studies Knowledge Base MCP Server"
+    SERVER_VERSION = "1.0.0"
+        
+    # Model path - adjust if your model is in a different location
+    MODEL_PATH = "google/embeddinggemma-300m"
+    
+    # ChromaDB path - relative to the base directory
+    CHROMADB_PATH = os.path.join(BASE_DIR, "chromadb_data")
+    
+    # Collection name in ChromaDB
+    COLLECTION_NAME = "rs_studies_embeddings"
+    
+    # Search Configuration
+    DEFAULT_NUM_RESULTS = 5
+    MAX_NUM_RESULTS = 50
+    DEFAULT_CONTEXT_SIZE = 3
+    MAX_CONTEXT_SIZE = 10
+    
+    # Valid source filters based on your data structure
+    VALID_SOURCES = [
+        'rs_stkege_01',        # RS trading system documentation
+        'cheenai_meet_full',   # Chennai meetup transcripts
+        'QnAYoutubeChannel'    # Q&A discussions
+    ]
+    
+    # Device configuration
+    @staticmethod
+    def get_device():
+        """Get the best available device for inference"""
+        if torch.cuda.is_available():
+            return "cuda"
+        else:
+            return "cpu"
+    
+    # Embedding configuration
+    EMBEDDING_BATCH_SIZE = 32
+
+# Create a default config instance
+config = Config()
+
+class ProdConfig(Config):
+    """Production configuration with optimized settings"""
+    MAX_NUM_RESULTS = 50
+    EMBEDDING_BATCH_SIZE = 64
+
+# Environment-based configuration selection
+def get_config(env="default"):
+    """Get configuration based on environment"""
+    return ProdConfig()