add graphs in the result and fix sources retrieved

app.py

@@ -89,7 +89,7 @@ user_id = create_user_id()
 # Create vectorstore and retriever
 vectorstore = get_pinecone_vectorstore(embeddings_function)
 llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0)
-reranker = get_reranker("nano")
+reranker = get_reranker("large")
 agent = make_graph_agent(llm,vectorstore,reranker)
 
 
@@ -232,24 +232,24 @@ async def chat(query,history,audience,sources,reports):
             except Exception as e:
                 print(f"Skipped adding image {i} because of {e}")
 
-    ## temp removing
-    # if len(image_dict) > 0:
-
-    #     gallery = [x["img"] for x in list(image_dict.values())]
-    #     img = list(image_dict.values())[0]
-    #     img_md = img["md"]
-    #     img_caption = img["caption"]
-    #     img_code = img["figure_code"]
-    #     if img_code != "N/A":
-    #         img_name = f"{img['key']} - {img['figure_code']}"
-    #     else:
-    #         img_name = f"{img['key']}"
-
-    #     answer_yet = history[-1][1] + f"\n\n{img_md}\n<p class='chatbot-caption'><b>{img_name}</b> - {img_caption}</p>"
-    #     history[-1] = (history[-1][0],answer_yet)
-    #     history = [tuple(x) for x in history]
+    if len(image_dict) > 0:
+
+        gallery = [x["img"] for x in list(image_dict.values())]
+        img = list(image_dict.values())[0]
+        img_md = img["md"]
+        img_caption = img["caption"]
+        img_code = img["figure_code"]
+        if img_code != "N/A":
+            img_name = f"{img['key']} - {img['figure_code']}"
+        else:
+            img_name = f"{img['key']}"
+
+        history.append(ChatMessage(role="assistant", content = f"\n\n{img_md}\n<p class='chatbot-caption'><b>{img_name}</b> - {img_caption}</p>"))
+        # answer_yet = history[-1][1] + f"\n\n{img_md}\n<p class='chatbot-caption'><b>{img_name}</b> - {img_caption}</p>"
+        # history[-1] = (history[-1][0],answer_yet)
+        # history = [tuple(x) for x in history]
 
-    # yield history,docs_html,output_query,output_language,gallery#,output_query,output_keywords
+    yield history,docs_html,output_query,output_language,gallery#,output_query,output_keywords
 
 
 def save_feedback(feed: str, user_id):
@@ -324,7 +324,7 @@ def vote(data: gr.LikeData):
 
 
 
-with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme,elem_id = "main-component") as demo:
+with gr.Blocks(title="Climate Q&A", css_paths=os.getcwd()+ "/style.css", theme=theme,elem_id = "main-component", js=js_script.js_code) as demo:
 
     with gr.Tab("ClimateQ&A"):
 

climateqa/engine/chains/retrieve_documents.py

@@ -82,11 +82,13 @@ def make_retriever_node(vectorstore,reranker,llm,rerank_by_question=True, k_fina
         # # Option 2 - Get 100/n documents by question and rerank the total
         # if rerank_by_question:
         #     k_by_question = divide_into_parts(k_final,len(questions))
+        if "documents" in state and state["documents"] is not None:
+            docs = state["documents"]
+        else:
+            docs = []
         
-        # docs = state["documents"]
-        # if docs is None: docs = []
-
-        docs = []
+        
+            
         k_by_question = k_final // state["n_questions"]
         
         sources = current_question["sources"]

climateqa/engine/llm/openai.py

@@ -7,7 +7,7 @@ try:
 except Exception:
     pass
 
-def get_llm(model="gpt-3.5-turbo-0125",max_tokens=1024, temperature=0.0, streaming=True,timeout=30, **kwargs):
+def get_llm(model="gpt-4o-mini",max_tokens=1024, temperature=0.0, streaming=True,timeout=30, **kwargs):
     
     llm = ChatOpenAI(
         model=model,