Update config.py

config.py

@@ -1,23 +1,60 @@
-from pathlib import Path
-
-class Config:
-    """Configuration class for all system settings"""
-    # File paths
-    KNOWLEDGE_DIR = Path("knowledge_base")  # Directory for all knowledge files
-    VECTOR_STORE_PATH = Path("vector_store")  # Directory for FAISS index
-    BM25_STORE_PATH = Path("vector_store/bm25.pkl")  # Serialized BM25 retriever
-    
-    # Text processing
-    CHUNK_SIZE = 1000  # Optimal for balance between context and retrieval
-    CHUNK_OVERLAP = 200
-    MAX_CONTEXT_CHUNKS = 5  # Number of chunks to retrieve
-    
-    # Performance
-    CACHE_EXPIRY_HOURS = 24
-    RELEVANCE_THRESHOLD = 0.72  # Strict similarity threshold
-    
-    @classmethod
-    def setup_dirs(cls):
-        """Ensure required directories exist"""
-        cls.KNOWLEDGE_DIR.mkdir(exist_ok=True)
-        cls.VECTOR_STORE_PATH.mkdir(exist_ok=True)
+import os
+import tempfile
+from pathlib import Path
+
+class Config:
+    # Use temporary directories for HF Spaces
+    BASE_DIR = Path(tempfile.gettempdir()) / "sirraya_xbrain"
+    
+    # Core directories
+    KNOWLEDGE_DIR = BASE_DIR / "knowledge"
+    VECTOR_STORE_PATH = BASE_DIR / "vector_store"
+    BM25_STORE_PATH = BASE_DIR / "bm25_store.pkl"
+    
+    # Processing parameters
+    CHUNK_SIZE = 1000
+    CHUNK_OVERLAP = 200
+    MAX_CONTEXT_CHUNKS = 5
+    
+    # Model settings
+    EMBEDDING_MODEL = "sentence-transformers/all-mpnet-base-v2"
+    FALLBACK_EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+    
+    # HF Models (in order of preference)
+    PRIMARY_LLM = "mistralai/Mistral-7B-Instruct-v0.1"
+    FALLBACK_LLMS = [
+        "microsoft/DialoGPT-medium",
+        "google/flan-t5-base",
+        "huggingface/CodeBERTa-small-v1"
+    ]
+    
+    # API settings
+    LLM_TEMPERATURE = 0.1
+    MAX_NEW_TOKENS = 512
+    REQUEST_TIMEOUT = 60
+    
+    @classmethod
+    def setup_dirs(cls):
+        """Setup required directories"""
+        cls.BASE_DIR.mkdir(parents=True, exist_ok=True)
+        cls.KNOWLEDGE_DIR.mkdir(parents=True, exist_ok=True)
+        cls.VECTOR_STORE_PATH.mkdir(parents=True, exist_ok=True)
+    
+    @classmethod
+    def get_hf_token(cls):
+        """Get HuggingFace API token from environment"""
+        return os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
+    
+    @classmethod
+    def cleanup_temp_dirs(cls):
+        """Cleanup temporary directories"""
+        try:
+            import shutil
+            if cls.BASE_DIR.exists():
+                shutil.rmtree(cls.BASE_DIR)
+        except Exception as e:
+            print(f"Cleanup error: {e}")
+    
+    # Environment-specific settings
+    IS_HF_SPACES = os.getenv("SPACE_ID") is not None
+    IS_LOCAL = not IS_HF_SPACES