made it asynchronous and resolved some dependency errors

.gitignore

@@ -0,0 +1 @@
+test_env/

app.py

@@ -10,6 +10,7 @@ from langchain_openai import OpenAIEmbeddings
 from langgraph.graph import END, StateGraph, START
 from langchain_core.output_parsers import StrOutputParser
 
+import asyncio
 from typing import List
 from typing_extensions import TypedDict
 import gradio as gr
@@ -105,7 +106,7 @@ def load_vectorstores(paths: list):
     return retrievers
         
 # Put all chains in fuctions
-def dora_rewrite(state):
+async def dora_rewrite(state):
     """
     Rewrites the question to fit dora wording
 
@@ -118,14 +119,14 @@ def dora_rewrite(state):
     print("---TRANSLATE TO DORA---")
     question = state["question"]
 
-    new_question = dora_question_rewriter.invoke({"question": question, "topics": TOPICS})
+    new_question = await dora_question_rewriter.ainvoke({"question": question, "topics": TOPICS})
 
     if new_question == "Thats an interesting question, but I dont think I can answer it based on my Dora knowledge.":
         return {"question": new_question, "generation": new_question}
     else:
         return {"question": new_question}
 
-def retrieve(state):
+async def retrieve(state):
     """
     Retrieve documents
 
@@ -142,15 +143,15 @@ def retrieve(state):
     # Retrieval
     documents = []
     if selected_sources[0]:
-        documents.extend(dora_retriever.invoke(question))
+        documents.extend(await dora_retriever.ainvoke(question))
     if selected_sources[1]:
-        documents.extend(dora_rts_retriever.invoke(question))
+        documents.extend(await dora_rts_retriever.ainvoke(question))
     if selected_sources[2]:
-        documents.extend(dora_news_retriever.invoke(question))
+        documents.extend(await dora_news_retriever.ainvoke(question))
 
     return {"documents": documents, "question": question}
 
-def grade_documents(state):
+async def grade_documents(state):
     """
     Determines whether the retrieved documents are relevant to the question.
 
@@ -169,7 +170,7 @@ def grade_documents(state):
 
     # Score each doc
     for d in documents:
-        score = retrieval_grader.invoke(
+        score = await retrieval_grader.ainvoke(
             {"question": question, "document": d.page_content}
         )
         grade = score.binary_score
@@ -185,7 +186,7 @@ def grade_documents(state):
 
     return {"fitting_documents": fitting_documents}
 
-def generate(state):
+async def generate(state):
     """
     Generate answer
 
@@ -204,10 +205,10 @@ def generate(state):
     dora_news_docs = [d for d in fitting_documents if d.metadata["source"].startswith("https")]
 
     # RAG generation
-    generation = answer_chain.invoke({"context": fitting_documents, "question": question})
+    generation = await answer_chain.ainvoke({"context": fitting_documents, "question": question})
     return {"generation": generation, "dora_docs": dora_docs, "dora_rts_docs": dora_rts_docs, "dora_news_docs": dora_news_docs}
 
-def transform_query(state):
+async def transform_query(state):
     """
     Transform the query to produce a better question.
 
@@ -222,12 +223,12 @@ def transform_query(state):
     question = state["question"]
 
     # Re-write question
-    better_question = question_rewriter.invoke({"question": question})
+    better_question = await question_rewriter.ainvoke({"question": question})
     print(f"{better_question =}")
     return {"question": better_question}
 
 ### Edges ###
-def suitable_question(state):
+async def suitable_question(state):
     """
     Determines whether the question is suitable.
 
@@ -246,7 +247,7 @@ def suitable_question(state):
     else:
         return "retrieve"
 
-def decide_to_generate(state):
+async def decide_to_generate(state):
     """
     Determines whether to generate an answer, or re-generate a question.
 
@@ -272,7 +273,7 @@ def decide_to_generate(state):
         print(f"---DECISION: GENERATE WITH {len(fitting_documents)} DOCUMENTS---")
         return "generate"
 
-def grade_generation_v_documents_and_question(state):
+async def grade_generation_v_documents_and_question(state):
     """
     Determines whether the generation is grounded in the document and answers question.
 
@@ -288,7 +289,7 @@ def grade_generation_v_documents_and_question(state):
     fitting_documents = state["fitting_documents"]
     generation = state["generation"]
 
-    score = hallucination_grader.invoke(
+    score = await hallucination_grader.ainvoke(
         {"documents": fitting_documents, "generation": generation}
     )
     grade = score.binary_score
@@ -298,7 +299,7 @@ def grade_generation_v_documents_and_question(state):
         print("---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---")
         # Check question-answering
         print("---GRADE GENERATION vs QUESTION---")
-        score = answer_grader.invoke({"question": question, "generation": generation})
+        score = await answer_grader.ainvoke({"question": question, "generation": generation})
         grade = score.binary_score
         if grade == "yes":
             print("---DECISION: GENERATION ADDRESSES QUESTION---")
@@ -313,6 +314,7 @@ def grade_generation_v_documents_and_question(state):
         print(f"{generation = }")
         return "not supported"
 
+# Then compile the graph
 def compile_graph():
     workflow = StateGraph(GraphState)
     # Define the nodes
@@ -355,9 +357,9 @@ def compile_graph():
     return app
 
 # Function to interact with Gradio
-def generate_response(question: str, dora: bool, rts: bool, news: bool):
+async def generate_response(question: str, dora: bool, rts: bool, news: bool):
     selected_sources = [dora, rts, news] if any([dora, rts, news]) else [True, False, False]
-    state = app.invoke({"question": question, "selected_sources": selected_sources})
+    state = await app.ainvoke({"question": question, "selected_sources": selected_sources})
     return (
         state["generation"],
         ('\n\n'.join([f"***{doc.metadata['source']} section {doc.metadata['section']}***: {doc.page_content}" for doc in state["dora_docs"]])) if "dora_docs" in state and state["dora_docs"] else 'No documents available.',

requirements.txt

@@ -5,3 +5,5 @@ langchain_openai==0.2.4
 langgraph==0.2.41
 pydantic==2.9.2
 typing_extensions==4.12.2
+faiss-cpu==1.8.0.post1
+asyncio==3.4.3