Update evaluate.py

evaluate.py

@@ -9,22 +9,47 @@ from typing import List, Dict, Any
 from pathlib import Path
 
 # --- Imports from the main application ---
+# In evaluate.py
+
 try:
     from alz_companion.agent import (
         make_rag_chain, route_query_type, detect_tags_from_query,
-        answer_query, call_llm, build_or_load_vectorstore  # <-- ADD IT HERE
+        answer_query, call_llm, build_or_load_vectorstore
     )
     from alz_companion.prompts import FAITHFULNESS_JUDGE_PROMPT
     from langchain_community.vectorstores import FAISS
+    # --- Also move this import inside the try block for consistency ---
+    from langchain.schema import Document
+
 except ImportError:
-    class FAISS: pass
+    # --- START: FALLBACK DEFINITIONS ---
+    class FAISS:
+        def __init__(self): self.docstore = type('obj', (object,), {'_dict': {}})()
+        def add_documents(self, docs): pass
+        def save_local(self, path): pass
+        @classmethod
+        def from_documents(cls, docs, embeddings=None): return cls()
+
+    class Document:
+        def __init__(self, page_content, metadata=None):
+            self.page_content = page_content
+            self.metadata = metadata or {}
+
     def make_rag_chain(*args, **kwargs): return lambda q, **k: {"answer": f"(Eval Fallback) You asked: {q}", "sources": []}
-    def route_query_type(q): return "general_conversation"
+    def route_query_type(q, **kwargs): return "general_conversation"
     def detect_tags_from_query(*args, **kwargs): return {}
     def answer_query(chain, q, **kwargs): return chain(q, **kwargs)
     def call_llm(*args, **kwargs): return "{}"
+    
+    # --- ADD FALLBACK DEFINITION FOR THE MISSING FUNCTION ---
+    def build_or_load_vectorstore(docs, index_path, is_personal=False):
+        return FAISS()
+    # --- END OF ADDITION ---
+
     FAITHFULNESS_JUDGE_PROMPT = ""
     print("WARNING: Could not import from alz_companion. Evaluation functions will use fallbacks.")
+    # --- END: FALLBACK DEFINITIONS ---
+    
 
 # --- LLM-as-a-Judge Prompt for Answer Correctness ---
 ANSWER_CORRECTNESS_JUDGE_PROMPT = """You are an expert evaluator. Your task is to assess the factual correctness of a generated answer against a ground truth answer.