fix

aimakerspace/openai_utils/chatmodel.py

@@ -9,11 +9,11 @@ class ChatOpenAI:
         if self.openai_api_key is None:
             raise ValueError("OPENAI_API_KEY is not set")
 
-    def run(self, client, messages, text_only: bool = True):
+    def run(self, messages, text_only: bool = True):
         if not isinstance(messages, list):
             raise ValueError("messages must be a list")
 
-        # client = OpenAI()
+        client = OpenAI()
         response = client.chat.completions.create(
             model=self.model_name, messages=messages
         )

app.py

@@ -8,6 +8,7 @@ import chainlit as cl  # importing chainlit for our app
 from chainlit.prompt import Prompt, PromptMessage  # importing prompt tools
 # from chainlit.playground.providers import ChatOpenAI  # importing ChatOpenAI tools
 from aimakerspace.openai_utils.chatmodel import ChatOpenAI
+
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -45,13 +46,13 @@ async def main(message: cl.Message):
     settings = cl.user_session.get("settings")
 
     # client = AsyncOpenAI()
-    client = ChatOpenAI()
+    chat_openai = ChatOpenAI()
 
     print(message.content)
 
     vector_db = _build_vector_db()
     pipeline = RetrievalAugmentedQAPipeline(
-        llm=client,
+        llm=chat_openai,
         vector_db_retriever=vector_db
     )
     response = pipeline.run_pipeline(message.content)

rag.py

@@ -41,7 +41,7 @@ class RetrievalAugmentedQAPipeline:
         self.llm = llm
         self.vector_db_retriever = vector_db_retriever
 
-    def run_pipeline(self, client, user_query: str) -> str:
+    def run_pipeline(self, user_query: str) -> str:
         context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
         
         context_prompt = ""
@@ -52,7 +52,7 @@ class RetrievalAugmentedQAPipeline:
 
         formatted_user_prompt = user_prompt.create_message(user_query=user_query)
         
-        return self.llm.run(client, [formatted_system_prompt, formatted_user_prompt])
+        return self.llm.run([formatted_system_prompt, formatted_user_prompt])
 
 def _split_documents():
     split_documents = []
@@ -71,10 +71,3 @@ def _build_vector_db():
     split_documents = _split_documents()
     vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))
     return vector_db
-
-# def retrieval_augmented_qa_pipeline(client):
-#     vector_db = _build_vector_db()
-#     pipeline = RetrievalAugmentedQAPipeline(
-#         llm=client,
-#         vector_db_retriever=vector_db)
-#     return pipeline