what if

rag.py

@@ -103,7 +103,7 @@ def get_random_chunk(scene_specific = [2]) : # scene_specific = None signifie qu
     return chunks[random.randint(0, len(chunks) - 1)],scene_specific
 
 
-def get_vectorstore() -> FAISS:
+def get_vectorstore(chunks) -> FAISS:
     index = faiss.IndexFlatL2(len(embedding.embed_query("hello world")))
     vector_store = FAISS(
         embedding_function=embedding,
@@ -116,7 +116,8 @@ def get_vectorstore() -> FAISS:
     vector_store.add_documents(documents=documents, ids=uuids)
     return vector_store
 
-vectore_store = get_vectorstore()
+vectore_store = get_vectorstore(chunks)
+scenes_vectore_store = get_vectorstore(scenes)
 
 
 def generate_sphinx_response() -> sphinx_output:
@@ -148,6 +149,10 @@ def retrieve_context_from_vectorestore(query:str) -> str:
     retriever = vectore_store.as_retriever(search_type="mmr", search_kwargs={"k": 3})
     return retriever.invoke(query)
 
+def retrieve_context_from_scenes(query:str) -> str:
+    retriever = scenes_vectore_store.as_retriever(search_kwargs={"k": 1})
+    return retriever.invoke(query)
+
         
 def generate_stream(query:str,messages = [], model = "gpt-4o-mini", max_tokens = 300, temperature = 1,index_name="",stream=True,vector_store=None):
     try:
@@ -179,7 +184,7 @@ def generate_whatif_stream(question:str,response:str, stream:bool = False) -> st
         prompt = PromptTemplate.from_template(template_whatif)
         llm_chain = prompt | llm | StrOutputParser()
         print("Enter whatif")
-        context = retrieve_context_from_vectorestore(f"{question} {response}")
+        context = retrieve_context_from_scenes(f"question: {question} . reponse : {response}")
         print(f"Context: {context}")
 
         if stream: