added chnages for open_ai key to avoid merge conflicts

.gitignore

@@ -0,0 +1,3 @@
+.env
+.vscode
+*.pyc

app.py

@@ -1,4 +1,4 @@
-
+from operator import index
 import streamlit as st
 import logging
 import os
@@ -10,10 +10,10 @@ from utils.config import parser
 from utils.haystack import start_document_store, query, initialize_pipeline, start_preprocessor_node, start_retriever, start_reader
 from utils.ui import reset_results, set_initial_state
 import pandas as pd
+import haystack
 
 # Whether the file upload should be enabled or not
 DISABLE_FILE_UPLOAD = bool(os.getenv("DISABLE_FILE_UPLOAD"))
-
 # Define a function to handle file uploads
 def upload_files():
     uploaded_files = st.sidebar.file_uploader(
@@ -49,28 +49,41 @@ def process_file(data_file, preprocesor, document_store):
 
 try:
     args = parser.parse_args()
-    #session_state = st.session_state
     preprocesor = start_preprocessor_node()
     document_store = start_document_store(type=args.store)
     retriever = start_retriever(document_store)
     reader = start_reader()
     st.set_page_config(
-        page_title="test",
+        page_title="MLReplySearch",
         layout="centered",
-        page_icon = (":shark:"),
+        page_icon=":shark:",
         menu_items={
-         'Get Help': 'https://www.extremelycoolapp.com/help',
-         'Report a bug': "https://www.extremelycoolapp.com/bug",
-         'About': "# This is a header. This is an *extremely* cool app!"
-     })
+            'Get Help': 'https://www.extremelycoolapp.com/help',
+            'Report a bug': "https://www.extremelycoolapp.com/bug",
+            'About': "# This is a header. This is an *extremely* cool app!"
+        }
+    )
     st.sidebar.image("ml_logo.png", use_column_width=True)
 
     # Sidebar for Task Selection
     st.sidebar.header('Options:')
-    task_selection = st.sidebar.radio('Select the task:', ['Extractive', 'Generative'])
 
-    pipeline_rag = initialize_pipeline("rag", document_store, retriever, reader)
-    pipeline_extractive = initialize_pipeline("extractive", document_store, retriever, reader)
+    # OpenAI Key Input
+    openai_key = st.sidebar.text_input("Enter OpenAI Key:", type="password")
+
+    if openai_key:
+        task_options = ['Extractive', 'Generative']
+    else:
+        task_options = ['Extractive']
+
+    task_selection = st.sidebar.radio('Select the task:', task_options)
+
+    # Check the task and initialize pipeline accordingly
+    if task_selection == 'Extractive':
+        pipeline_extractive = initialize_pipeline("extractive", document_store, retriever, reader)
+    elif task_selection == 'Generative' and openai_key:  # Check for openai_key to ensure user has entered it
+        pipeline_rag = initialize_pipeline("rag", document_store, retriever, reader, openai_key=openai_key)
+
 
     set_initial_state()
 
@@ -138,9 +151,12 @@ try:
                         "👓    An error occurred reading the results. Is the document store working?"
                     )
                 except Exception as e:
-                    logging.exception(e)
-                    st.error("🐞    An error occurred during the request.")
-
+                    if "API key is invalid" in str(e):
+                        logging.exception(e)
+                        st.error("🐞    incorrect API key provided. You can find your API key at https://platform.openai.com/account/api-keys.")
+                    else:
+                        logging.exception(e)
+                        st.error("🐞    An error occurred during the request.")
     # Display results
     if (st.session_state.results_extractive or st.session_state.results_generative) and run_query:
 

utils/config.py

@@ -7,9 +7,7 @@ load_dotenv()
 parser = argparse.ArgumentParser(description='This app lists animals')
 
 document_store_choices = ('inmemory', 'weaviate', 'milvus', 'opensearch')
-task_choices = ('extractive', 'rag')
 parser.add_argument('--store', choices=document_store_choices, default='inmemory', help='DocumentStore selection (default: %(default)s)')
-#parser.add_argument('--task', choices=task_choices, default='rag', help='Task selection (default: %(default)s)')
 parser.add_argument('--name', default="My Search App")
 
 model_configs = {

utils/haystack.py

@@ -32,6 +32,7 @@ def start_document_store(type: str):
     print('initializing document store')
     if type == 'inmemory':
         document_store = InMemoryDocumentStore(use_bm25=True, embedding_dim=384)
+        '''
         documents = [
             {
                 'content': "Pi is a super dog",
@@ -42,7 +43,8 @@ def start_document_store(type: str):
                 'meta': {'name': "siemens.txt"}
             },
         ]
-        #document_store.write_documents(documents)
+        document_store.write_documents(documents)
+        '''
     elif type == 'opensearch':
         document_store = OpenSearchDocumentStore(scheme = document_store_configs['OPENSEARCH_SCHEME'], 
                                                  username = document_store_configs['OPENSEARCH_USERNAME'], 
@@ -94,10 +96,10 @@ def start_haystack_extractive(_document_store: BaseDocumentStore, _retriever: Em
     return pipe
 
 @st.cache_resource(show_spinner=False)
-def start_haystack_rag(_document_store: BaseDocumentStore, _retriever: EmbeddingRetriever):
+def start_haystack_rag(_document_store: BaseDocumentStore, _retriever: EmbeddingRetriever, openai_key):
     prompt_node = PromptNode(default_prompt_template="deepset/question-answering", 
                              model_name_or_path=model_configs['GENERATIVE_MODEL'],
-                             api_key=model_configs['OPENAI_KEY'])
+                             api_key=openai_key)
     pipe = Pipeline()
 
     pipe.add_node(component=_retriever, name="Retriever", inputs=["Query"])
@@ -111,8 +113,8 @@ def query(_pipeline, question):
     results = _pipeline.run(question, params=params)
     return results
 
-def initialize_pipeline(task, document_store, retriever, reader):
+def initialize_pipeline(task, document_store, retriever, reader, openai_key = ""):
     if task == 'extractive':
         return start_haystack_extractive(document_store, retriever, reader)
     elif task == 'rag':
-        return start_haystack_rag(document_store, retriever)
+        return start_haystack_rag(document_store, retriever, openai_key)