Update vecstore.py

vecstore.py

@@ -1,42 +1,56 @@
 """Utilities for creating and using vector indexes."""
 from pathlib import Path
-
+from typing import List
 from utils import pretty_log
 
 INDEX_NAME = "openai-ada-fsdl"
 VECTOR_DIR = Path("/vectors")
 
 
-def connect_to_vector_index(index_name, embedding_engine):
+def connect_to_vector_index(index_name: str, embedding_engine) -> 'FAISS':
     """Adds the texts and metadatas to the vector index."""
     from langchain.vectorstores import FAISS
 
-    vector_index = FAISS.load_local(VECTOR_DIR, embedding_engine, index_name)
-
-    return vector_index
+    try:
+        vector_index = FAISS.load_local(VECTOR_DIR, embedding_engine, index_name)
+        return vector_index
+    except Exception as e:
+        pretty_log(f"Error connecting to vector index: {e}")
+        return None
 
 
-def get_embedding_engine(model="text-embedding-ada-002", **kwargs):
+def get_embedding_engine(model: str = "text-embedding-ada-002", **kwargs) -> 'OpenAIEmbeddings':
     """Retrieves the embedding engine."""
     from langchain.embeddings import OpenAIEmbeddings
 
-    embedding_engine = OpenAIEmbeddings(model=model, **kwargs)
-
-    return embedding_engine
+    try:
+        embedding_engine = OpenAIEmbeddings(model=model, **kwargs)
+        return embedding_engine
+    except Exception as e:
+        pretty_log(f"Error initializing embedding engine: {e}")
+        return None
 
 
-def create_vector_index(index_name, embedding_engine, documents, metadatas):
+def create_vector_index(index_name: str, embedding_engine, documents: List[str], metadatas: List[dict]) -> 'FAISS':
     """Creates a vector index that offers similarity search."""
-    from langchain import FAISS
+    from langchain.vectorstores import FAISS
 
-    files = VECTOR_DIR.glob(f"{index_name}.*")
+    # Clean up existing index files if any
+    files = list(VECTOR_DIR.glob(f"{index_name}.*"))
     if files:
         for file in files:
-            file.unlink()
-        pretty_log("existing index wiped")
-
-    index = FAISS.from_texts(
-        texts=documents, embedding=embedding_engine, metadatas=metadatas
-    )
-
-    return index
+            try:
+                file.unlink()
+                pretty_log(f"Deleted existing index file: {file.name}")
+            except Exception as e:
+                pretty_log(f"Error deleting file {file.name}: {e}")
+
+    # Create a new index
+    try:
+        index = FAISS.from_texts(
+            texts=documents, embedding=embedding_engine, metadatas=metadatas
+        )
+        return index
+    except Exception as e:
+        pretty_log(f"Error creating vector index: {e}")
+        return None