try to fix sql error

app.py

@@ -9,65 +9,6 @@ doc_dir = "data/wiki_gameofthrones_txt12"
 sql_file = 'faiss_doc_store.db'
 faiss_file = 'faiss_index.faiss'
 
-# %% ------------------------------------------- Creating Doc store
-if not os.path.exists(sql_file) or not os.path.exists(faiss_file):
-    from haystack.document_stores import FAISSDocumentStore
-    from haystack.nodes import DensePassageRetriever
-    from haystack.utils import convert_files_to_docs, clean_wiki_text
-
-
-    module_dir = os.path.dirname(os.path.abspath(__file__))
-    os.chdir(module_dir) 
-
-
-
-    # %% Download/Load Docs
-
-    # Get some files that we want to use
-    # s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt12.zip"
-    # fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-    print('---> Loading Documents ...')
-
-    # Convert files to docs + cleaning
-    docs = convert_files_to_docs(dir_path=doc_dir,
-                                clean_func=clean_wiki_text,
-                                split_paragraphs=True)
-
-    # %% Document Store
-
-    print('---> Creating document store ...')
-    document_store = FAISSDocumentStore(embedding_dim=128,
-                                        faiss_index_factory_str="Flat",
-                                        sql_url=f"sqlite:///{sql_file}")
-
-
-
-    # %% Retriever (DPR)
-
-    print('---> Initializing retriever ...')
-    retriever = DensePassageRetriever(
-        document_store=document_store,
-        query_embedding_model="vblagoje/dpr-question_encoder-single-lfqa-wiki",
-        passage_embedding_model="vblagoje/dpr-ctx_encoder-single-lfqa-wiki",
-        use_gpu=True
-    )
-
-    # %% Create Embeddings  and save results
-    document_store.update_embeddings(retriever)
-
-    print('---> Saving results ...')
-    # update db
-    document_store.write_documents(docs)
-    # save faiss file
-    document_store.save(faiss_file)
-
-    print('Done!')
-
-
-# %% ------------------------------------------- Main App
-
-
 # Sliders
 DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3"))
 # Adjust to a question that you would like users to see in the search bar when they load the UI:
@@ -181,5 +122,64 @@ def main(pipe):
             st.write(st.session_state.results['answers'][0].meta['content'][i])
             st.markdown('---\n')
 
-pipe = prepare()
+# %% ------------------------------------------- Creating Doc store
+# if not os.path.exists(sql_file) or not os.path.exists(faiss_file):
+from haystack.document_stores import FAISSDocumentStore
+from haystack.nodes import DensePassageRetriever
+from haystack.utils import convert_files_to_docs, clean_wiki_text
+
+
+module_dir = os.path.dirname(os.path.abspath(__file__))
+os.chdir(module_dir) 
+
+
+
+# %% Download/Load Docs
+
+# Get some files that we want to use
+# s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt12.zip"
+# fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
+
+print('---> Loading Documents ...')
+
+# Convert files to docs + cleaning
+docs = convert_files_to_docs(dir_path=doc_dir,
+                            clean_func=clean_wiki_text,
+                            split_paragraphs=True)
+
+# %% Document Store
+
+print('---> Creating document store ...')
+document_store = FAISSDocumentStore(embedding_dim=128,
+                                    faiss_index_factory_str="Flat",
+                                    sql_url=f"sqlite:///{sql_file}")
+
+
+
+# %% Retriever (DPR)
+
+print('---> Initializing retriever ...')
+retriever = DensePassageRetriever(
+    document_store=document_store,
+    query_embedding_model="vblagoje/dpr-question_encoder-single-lfqa-wiki",
+    passage_embedding_model="vblagoje/dpr-ctx_encoder-single-lfqa-wiki",
+    use_gpu=True
+)
+
+# %% Create Embeddings  and save results
+document_store.update_embeddings(retriever)
+
+print('---> Saving results ...')
+# update db
+document_store.write_documents(docs)
+# save faiss file
+document_store.save(faiss_file)
+
+print('Done!')
+
+
+# %% ------------------------------------------- Main App
+
+pipe = GenerativeQAPipeline(generator, retriever)
+# pipe = prepare()
 main(pipe)