third commit

earnings_app.py

@@ -104,38 +104,37 @@ service_context = ServiceContext.from_defaults(
 index = GPTVectorStoreIndex.from_documents([], service_context=service_context)
 
 
-storage_context = wandb_callback.load_storage_context(
-    artifact_url="llmop/final-project-v1/earnings-index:v3"
-)
+# Main function to extract information
+def extract_information():
+    # Make sure to use a recent model that supports tools
 
-index = load_index_from_storage(storage_context, service_context=service_context)
+    storage_context = wandb_callback.load_storage_context(
+        artifact_url="llmop/final-project-v1/earnings-index:v3"
+    )
 
-def auto_retrieve_fn(
-    query: str, filter_key_list: List[str], filter_value_list: List[str]
-):
-    """Auto retrieval function.
+    index = load_index_from_storage(storage_context, service_context=service_context)
 
-    Performs auto-retrieval from a vector database, and then applies a set of filters.
+    def auto_retrieve_fn(
+        query: str, filter_key_list: List[str], filter_value_list: List[str]
+    ):
+        """Auto retrieval function.
 
-    """
-    query = query or "Query"
+        Performs auto-retrieval from a vector database, and then applies a set of filters.
 
-    exact_match_filters = [
-        ExactMatchFilter(key=k, value=v)
-        for k, v in zip(filter_key_list, filter_value_list)
-    ]
-    retriever = VectorIndexRetriever(
-        index, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k
-    )
-    query_engine = RetrieverQueryEngine.from_args(retriever, service_context=service_context)
-
-    response = query_engine.query(query)
-    return str(response)
+        """
+        query = query or "Query"
 
+        exact_match_filters = [
+            ExactMatchFilter(key=k, value=v)
+            for k, v in zip(filter_key_list, filter_value_list)
+        ]
+        retriever = VectorIndexRetriever(
+            index, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k
+        )
+        query_engine = RetrieverQueryEngine.from_args(retriever, service_context=service_context)
 
-# Main function to extract information
-def extract_information():
-    # Make sure to use a recent model that supports tools
+        response = query_engine.query(query)
+        return str(response)
     
     auto_retrieve_tool = FunctionTool.from_defaults(
         fn=auto_retrieve_fn,