add search only button

app.py

@@ -118,7 +118,7 @@ reranker = get_reranker("nano")
 agent = make_graph_agent(llm=llm, vectorstore_ipcc=vectorstore, vectorstore_graphs=vectorstore_graphs, reranker=reranker)
 
 
-async def chat(query, history, audience, sources, reports, relevant_content_sources):
+async def chat(query, history, audience, sources, reports, relevant_content_sources, search_only):
     """taking a query and a message history, use a pipeline (reformulation, retriever, answering) to yield a tuple of:
     (messages in gradio format, messages in langchain format, source documents)"""
 
@@ -134,7 +134,7 @@ async def chat(query, history, audience, sources, reports, relevant_content_sour
     if reports is None or len(reports) == 0:
         reports = []
     
-    inputs = {"user_input": query,"audience": audience_prompt,"sources_input":sources, "relevant_content_sources" : relevant_content_sources}
+    inputs = {"user_input": query,"audience": audience_prompt,"sources_input":sources, "relevant_content_sources" : relevant_content_sources, "search_only": search_only}
     result = agent.astream_events(inputs,version = "v1") 
 
 
@@ -569,14 +569,14 @@ with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=t
     
     (textbox
         .submit(start_chat, [textbox,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_textbox")
-        .then(chat, [textbox,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox")
+        .then(chat, [textbox,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources, search_only] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox")
         .then(finish_chat, None, [textbox],api_name = "finish_chat_textbox")
         # .then(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_graphs, tab_papers] )
     )
 
     (examples_hidden
         .change(start_chat, [examples_hidden,chatbot], [textbox,tabs,chatbot],queue = False,api_name = "start_chat_examples")
-        .then(chat, [examples_hidden,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox")
+        .then(chat, [examples_hidden,chatbot,dropdown_audience, dropdown_sources,dropdown_reports, dropdown_external_sources, search_only] ,[chatbot,sources_textbox,output_query,output_language, sources_raw, current_graphs],concurrency_limit = 8,api_name = "chat_textbox")
         .then(finish_chat, None, [textbox],api_name = "finish_chat_examples")
         # .then(update_sources_number_display, [sources_textbox, figures_cards, current_graphs,papers_html],[tab_sources, tab_figures, tab_graphs, tab_papers] )
     )

climateqa/engine/graph.py

@@ -20,7 +20,7 @@ from .chains.retrieve_documents import make_retriever_node
 from .chains.answer_rag import make_rag_node
 from .chains.graph_retriever import make_graph_retriever_node
 from .chains.chitchat_categorization import make_chitchat_intent_categorization_node
-from .chains.set_defaults import set_defaults
+# from .chains.set_defaults import set_defaults
 
 class GraphState(TypedDict):
     """
@@ -43,6 +43,7 @@ class GraphState(TypedDict):
     documents: List[Document]
     related_contents : Dict[str,Document]
     recommended_content : List[Document]
+    search_only : bool = False
 
 def search(state): #TODO
     return state
@@ -80,6 +81,13 @@ def route_based_on_relevant_docs(state,threshold_docs=0.2):
     else:
         return "answer_rag_no_docs"
     
+def route_retrieve_documents(state):
+    if state["search_only"] :
+        return END
+    elif len(state["remaining_questions"]) > 0:
+        return "retrieve_documents"
+    else:
+        return "answer_search"
 
 def make_id_dict(values):
     return {k:k for k in values}
@@ -138,8 +146,9 @@ def make_graph_agent(llm, vectorstore_ipcc, vectorstore_graphs, reranker, thresh
     )
     workflow.add_conditional_edges(
         "retrieve_documents",
-        lambda state : "retrieve_documents" if len(state["remaining_questions"]) > 0 else "answer_search",
-        make_id_dict(["retrieve_documents","answer_search"])
+        # lambda state : "retrieve_documents" if len(state["remaining_questions"]) > 0 else "answer_search",
+        route_retrieve_documents,
+        make_id_dict([END,"retrieve_documents","answer_search"])
     )
 
     workflow.add_conditional_edges(

style.css

@@ -561,6 +561,10 @@ span.chatbot > p > img{
   input[type="checkbox"]:checked + .dropdown-content {
     display: block;
   }
+
+  #checkbox-chat input[type="checkbox"] {
+    display: flex !important;
+  }
   
   .dropdown-content {
     display: none;