update to new preview

app.py

@@ -9,7 +9,7 @@ environ['OPENAI_API_BASE'] = st.secrets['OPENAI_API_BASE']
 from langchain.vectorstores import MyScale, MyScaleSettings
 from langchain.embeddings import HuggingFaceInstructEmbeddings
 from langchain.retrievers.self_query.base import SelfQueryRetriever
-from langchain.chains.query_constructor.base import AttributeInfo
+from langchain.chains.query_constructor.base import AttributeInfo, VirtualColumnName
 from langchain import OpenAI
 from langchain.chat_models import ChatOpenAI
 
@@ -19,9 +19,9 @@ from langchain.prompts import PromptTemplate, ChatPromptTemplate, \
 from sqlalchemy import create_engine, MetaData
 from langchain.chains import LLMChain
 
-from langchain_experimental.utilities.sql_database import SQLDatabase
-from langchain_experimental.retrievers.sql_database import SQLDatabaseChainRetriever
-from langchain_experimental.sql.base import SQLDatabaseChain
+from langchain.utilities.sql_database import SQLDatabase
+from langchain_experimental.retrievers.vector_sql_database import VectorSQLDatabaseChainRetriever
+from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
 
 from chains.arxiv_chains import VectorSQLRetrieveCustomOutputParser
 from chains.arxiv_chains import ArXivQAwithSourcesChain, ArXivStuffDocumentChain
@@ -82,7 +82,7 @@ def build_retriever():
     with st.spinner("Building Self Query Retriever..."):
         metadata_field_info = [
             AttributeInfo(
-                name="pubdate",
+                name=VirtualColumnName(name="pubdate"),
                 description="The year the paper is published",
                 type="timestamp",
             ),
@@ -155,7 +155,7 @@ def build_retriever():
 
         output_parser = VectorSQLRetrieveCustomOutputParser.from_embeddings(
             model=embeddings)
-        sql_query_chain = SQLDatabaseChain.from_llm(
+        sql_query_chain = VectorSQLDatabaseChain.from_llm(
             llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),
             prompt=PROMPT,
             top_k=10,
@@ -164,7 +164,7 @@ def build_retriever():
             sql_cmd_parser=output_parser,
             native_format=True
         )
-        sql_retriever = SQLDatabaseChainRetriever(
+        sql_retriever = VectorSQLDatabaseChainRetriever(
             sql_db_chain=sql_query_chain, page_content_key="abstract")
 
     with st.spinner('Building QA Chain with Vector SQL...'):
@@ -184,7 +184,8 @@ def build_retriever():
             max_tokens_limit=12000,
         )
 
-    return [{'name': m.name, 'desc': m.description, 'type': m.type} for m in metadata_field_info], retriever, chain, sql_retriever, sql_chain
+    return [{'name': m.name.name if type(m.name) is VirtualColumnName else m.name, 'desc': m.description, 'type': m.type} for m in metadata_field_info], \
+        retriever, chain, sql_retriever, sql_chain
 
 
 if 'retriever' not in st.session_state:

chains/arxiv_chains.py

@@ -15,7 +15,7 @@ from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesCha
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 
-from langchain_experimental.sql.parser import VectorSQLOutputParser
+from langchain_experimental.sql.vector_sql import VectorSQLOutputParser
 
 
 class VectorSQLRetrieveCustomOutputParser(VectorSQLOutputParser):