Reverts

app.py

@@ -1,60 +1,36 @@
-# from typing import List
-# from chainlit.types import AskFileResponse
+import os
+from typing import List
+from chainlit.types import AskFileResponse
 from aimakerspace.text_utils import CharacterTextSplitter, PDFFileLoader
 from aimakerspace.openai_utils.prompts import (
     UserRolePrompt,
     SystemRolePrompt,
+    AssistantRolePrompt,
 )
 from aimakerspace.openai_utils.embedding import EmbeddingModel
 from aimakerspace.vectordatabase import VectorDatabase
 from aimakerspace.openai_utils.chatmodel import ChatOpenAI
 import chainlit as cl
-# import asyncio
-# from operator import itemgetter
+import asyncio
 import nest_asyncio
 nest_asyncio.apply()
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 
-filepath_NIST = "data/NIST.AI.600-1.pdf"
-filepath_Blueprint = "data/Blueprint-for-an-AI-Bill-of-Rights.pdf"
+pdf_loader_NIST = PDFFileLoader("data/NIST.AI.600-1.pdf")
+pdf_loader_Blueprint = PDFFileLoader("data/Blueprint-for-an-AI-Bill-of-Rights.pdf")
+documents_NIST = pdf_loader_NIST.load_documents()
+documents_Blueprint = pdf_loader_Blueprint.load_documents()
 
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size = 500,
-    chunk_overlap = 50
-)
-
-documents_NIST = PyMuPDFLoader(filepath_NIST).load()
-documents_Blueprint = PyMuPDFLoader(filepath_Blueprint).load()
-
-split_NIST = text_splitter.split_documents(documents_NIST)
-split_Blueprint = text_splitter.split_documents(documents_Blueprint)
-
-# embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
-
-# vectorstore = Qdrant.from_documents(
-#     documents=rag_documents,
-#     embedding=embeddings,
-#     location=":memory:",
-#     collection_name="Implications of AI"
-# )
-# retriever = qdrant_vectorstore.as_retriever()
-
-RAG_PROMPT = """\
-Given a provided context and question, you must answer the question based only on context.
-
-If you cannot answer the question based on the context - you must say "I don't know".
+text_splitter = CharacterTextSplitter()
+split_documents_NIST = text_splitter.split_texts(documents_NIST)
+split_documents_Blueprint = text_splitter.split_texts(documents_Blueprint)
 
-Context: {context}
-Question: {question}
-"""
-
-# prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
 
 RAG_PROMPT_TEMPLATE = """ \
 Use the provided context to answer the user's query.
+
 You may not answer the user's query unless there is specific context in the following text.
+
 If you do not know the answer, or cannot answer, please respond with "I don't know".
 """
 
@@ -63,6 +39,7 @@ rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
 USER_PROMPT_TEMPLATE = """ \
 Context:
 {context}
+
 User Query:
 {user_query}
 """
@@ -106,25 +83,21 @@ async def start_chat():
     #     "presence_penalty": 0,
     # }
 
-    # # Create a dict vector store
+    # Create a dict vector store
     vector_db = VectorDatabase()
-    # vector_db = await vector_db.abuild_from_list(rag_documents)
-    vector_db = await vector_db.abuild_from_list(split_NIST)
-    vector_db = await vector_db.abuild_from_list(split_Blueprint)
+    vector_db = await vector_db.abuild_from_list(split_documents_NIST)
+    vector_db = await vector_db.abuild_from_list(split_documents_Blueprint)
     
-    # # chat_openai = ChatOpenAI()
-    llm = ChatOpenAI(model="gpt-4o-mini", tags=["base_llm"]) 
-
+    chat_openai = ChatOpenAI()
 
-    # # Create a chain
-    rag_chain = RetrievalAugmentedQAPipeline(
+    # Create a chain
+    retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
         vector_db_retriever=vector_db,
-        llm=llm
+        llm=chat_openai
     )
 
-
     # cl.user_session.set("settings", settings)
-    cl.user_session.set("chain", rag_chain)
+    cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
 
 
 @cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user