Metrics Calculation script

llm_docker_generator/templates/llms.proto.j2

@@ -32,12 +32,8 @@ message LLMQuery {
 
 message LLMAnswer {
     string text = 1;
-<<<<<<< HEAD
     ConvoID id = 2; 
     string relevant_context = 3;
-=======
-    ConvoID id = 2;  
->>>>>>> 509e561080ec6ceab6019fc084096e7c8ccb2503
 }
 
 message Status {

llm_docker_generator/templates/rag_chain.py.j2

@@ -28,7 +28,6 @@ def faiss_db():
 def format_docs(docs):
     return "\n\n".join(doc.page_content for doc in docs)
 
-<<<<<<< HEAD
 def get_context(retriever, question):
     docs = retriever.get_relevant_documents(question)
     
@@ -36,8 +35,6 @@ def get_context(retriever, question):
     context = "\n".join([doc.page_content for doc in docs])
     return context
 
-=======
->>>>>>> 509e561080ec6ceab6019fc084096e7c8ccb2503
 
 def rag_chain(retriever, system_instruction, llm_service, question):
     prompt_template_generic = """
@@ -67,11 +64,7 @@ def rag_chain(retriever, system_instruction, llm_service, question):
     # Invoke the chain with the provided question
     rag_ans = rag_chain.invoke(question)
 
-<<<<<<< HEAD
     # Get relevant context
     relevant_context= get_context(retriever, question)
 
     return rag_ans, relevant_context
-=======
-    return rag_ans
->>>>>>> 509e561080ec6ceab6019fc084096e7c8ccb2503

llm_docker_generator/templates/server.py.j2

@@ -7,7 +7,6 @@ import llms_pb2
 from app import app_run
 from apps.application_manager import LLMApplicationManager
 
-<<<<<<< HEAD
 class LLMServiceServicer(llms_pb2_grpc.LLMServiceServicer):
     def __init__(self, app_type="invoke_direct_llm"):
         self.application = LLMApplicationManager.get_application(app_type)  # Returns a class 
@@ -24,24 +23,6 @@ class LLMServiceServicer(llms_pb2_grpc.LLMServiceServicer):
             )
 
 
-=======
-
-class LLMServiceServicer(llms_pb2_grpc.LLMServiceServicer):
-    def __init__(self, app_type="invoke_direct_llm"):
-        self.application = LLMApplicationManager.get_application(app_type)
-
-    def instruct_llm_stream(self, request_iterator, context):
-        for llm_query in request_iterator:
-            response_text = self.application.process_request(llm_query)
-            print(
-                f"Processed: {llm_query.input.prompt} with question: {llm_query.qa.question}"
-            )
-            yield llms_pb2.LLMAnswer(
-                id=llms_pb2.ConvoID(q_id=llm_query.id.q_id), text=response_text
-            )
-
-
->>>>>>> 509e561080ec6ceab6019fc084096e7c8ccb2503
 def serve(port, app_type="invoke_direct_llm"):
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
     llms_pb2_grpc.add_LLMServiceServicer_to_server(LLMServiceServicer(app_type), server)