Update src/langchain_utils.py

src/langchain_utils.py

@@ -13,7 +13,7 @@ import chromadb
 from langchain.chains.question_answering import load_qa_chain
 from langchain.chains.qa_with_sources import load_qa_with_sources_chain
 from langchain.prompts import PromptTemplate
-from llama_index import GPTSimpleVectorIndex, GPTListIndex
+from llama_index import GPTVectorStoreIndex, GPTListIndex
 from langchain.vectorstores import FAISS
 
 import pickle
@@ -329,8 +329,8 @@ class LANGCHAIN_UTILS:
             )
 
         # Vector store using GPT vector index
-        elif self.index_type == 'GPTSimpleVectorIndex':
-            self.index = GPTSimpleVectorIndex.from_documents(self.documents)
+        elif self.index_type == 'GPTVectorStoreIndex':
+            self.index = GPTVectorStoreIndex.from_documents(self.documents)
 
         logger.info(f'Index created successfully!')
         return self.index
@@ -486,7 +486,7 @@ class LANGCHAIN_UTILS:
         elif self.index_type == 'Chroma':
             index.persist()
 
-        elif self.index_type == 'GPTSimpleVectorIndex':
+        elif self.index_type == 'GPTVectorStoreIndex':
             index.save_to_disk(index_filepath)
 
         elif self.index_type == 'pickle':
@@ -514,8 +514,8 @@ class LANGCHAIN_UTILS:
                 embedding_function=self.embeddings
             )
 
-        elif self.index_type == 'GPTSimpleVectorIndex':
-            self.index = GPTSimpleVectorIndex.load_from_disk(self.index_filepath)
+        elif self.index_type == 'GPTVectorStoreIndex':
+            self.index = GPTVectorStoreIndex.load_from_disk(self.index_filepath)
 
         elif self.index_type == 'pickle':
             with open(self.index_filepath, "rb") as f:
@@ -543,9 +543,9 @@ class LANGCHAIN_UTILS:
         url_documents
     ):
         # Build the Vector store for docs
-        doc_index = GPTSimpleVectorIndex.from_documents(doc_documents)
+        doc_index = GPTVectorStoreIndex.from_documents(doc_documents)
         # Build the Vector store for URLs
-        url_index = GPTSimpleVectorIndex.from_documents(url_documents)
+        url_index = GPTVectorStoreIndex.from_documents(url_documents)
 
         # Set summary of each index
         doc_index.set_text("index_from_docs")
@@ -592,10 +592,10 @@ class LANGCHAIN_UTILS:
                     continue
                 raise NotImplementedError
 
-        elif self.index_type == 'GPTSimpleVectorIndex':
+        elif self.index_type == 'GPTVectorStoreIndex':
             for doc_type, index in doc_type_indices.items():
-                if not index or not isinstance(index, GPTSimpleVectorIndex):
-                    logger.warning(f'{doc_type} index to be merged is not an instance of type llama_index.GPTSimpleVectorIndex')
+                if not index or not isinstance(index, GPTVectorStoreIndex):
+                    logger.warning(f'{doc_type} index to be merged is not an instance of type llama_index.GPTVectorStoreIndex')
                     continue
                 import pdb; pdb.set_trace()
                 raise NotImplementedError
@@ -676,7 +676,7 @@ class LANGCHAIN_UTILS:
                 k=similarity_top_k
             )
 
-        elif self.index_type == 'GPTSimpleVectorIndex':
+        elif self.index_type == 'GPTVectorStoreIndex':
             # Querying the index
             response = index.query(
                 question,