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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ac0cc1aa-e68d-432d-b316-52e272c43207",
"metadata": {},
"outputs": [],
"source": [
"import streamlit as st\n",
"from streamlit_feedback import streamlit_feedback\n",
"\n",
"import os\n",
"import pandas as pd\n",
"import base64\n",
"from io import BytesIO\n",
"import sys\n",
"sys.path.insert(0, \"../\")\n",
"\n",
"import chromadb\n",
"from llama_index.core import (\n",
" VectorStoreIndex, \n",
" SimpleDirectoryReader,\n",
" StorageContext,\n",
" Document\n",
")\n",
"from llama_index.vector_stores.chroma.base import ChromaVectorStore\n",
"from llama_index.embeddings.huggingface.base import HuggingFaceEmbedding\n",
"from llama_index.llms.openai import OpenAI\n",
"from llama_index.core.memory import ChatMemoryBuffer\n",
"from llama_index.core.tools import QueryEngineTool\n",
"from llama_index.agent.openai import OpenAIAgent\n",
"from llama_index.core import Settings\n",
"\n",
"from vision_api import get_transcribed_text\n",
"from qna_prompting import get_qna_question_tool, evaluate_qna_answer_tool\n",
"\n",
"import nest_asyncio\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b05cb9b-869a-409c-8d4f-aafae703c558",
"metadata": {},
"outputs": [],
"source": [
"@st.cache_resource\n",
"def get_document_object(input_files):\n",
" documents = SimpleDirectoryReader(input_files=input_files).load_data()\n",
" document = Document(text=\"\\n\\n\".join([doc.text for doc in documents]))\n",
" return document\n",
"\n",
"@st.cache_resource\n",
"def get_llm_object(selected_model, temperature):\n",
" llm = OpenAI(model=selected_model, temperature=temperature)\n",
" return llm\n",
"\n",
"@st.cache_resource\n",
"def get_embedding_model(model_name, fine_tuned_path=None):\n",
" if fine_tuned_path is None:\n",
" print(f\"loading from `{model_name}` from huggingface\")\n",
" embed_model = HuggingFaceEmbedding(model_name=model_name)\n",
" else:\n",
" print(f\"loading from local `{fine_tuned_path}`\")\n",
" embed_model = fine_tuned_path\n",
" return embed_model\n",
"\n",
"@st.cache_resource\n",
"def get_query_engine(input_files, llm_model, temperature,\n",
" embedding_model, fine_tuned_path,\n",
" system_content, persisted_vector_db):\n",
" \n",
" llm = get_llm_object(llm_model, temperature)\n",
" embedded_model = get_embedding_model(\n",
" model_name=embedding_model, \n",
" fine_tuned_path=fine_tuned_path\n",
" )\n",
" Settings.llm = llm\n",
" Settings.chunk_size = 1024\n",
" Settings.embed_model = embedded_model\n",
"\n",
" if os.path.exists(persisted_vector_db):\n",
" print(\"loading from vector database - chroma\")\n",
" db = chromadb.PersistentClient(path=persisted_vector_db)\n",
" chroma_collection = db.get_or_create_collection(\"quickstart\")\n",
" vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
" storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"\n",
" index = VectorStoreIndex.from_vector_store(\n",
" vector_store=vector_store,\n",
" storage_context=storage_context\n",
" )\n",
" else:\n",
" print(\"create new chroma vector database..\")\n",
" documents = SimpleDirectoryReader(input_files=input_files).load_data()\n",
" \n",
" db = chromadb.PersistentClient(path=persisted_vector_db)\n",
" chroma_collection = db.get_or_create_collection(\"quickstart\")\n",
" vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
" \n",
" nodes = Settings.node_parser.get_nodes_from_documents(documents)\n",
" storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
" storage_context.docstore.add_documents(nodes)\n",
"\n",
" index = VectorStoreIndex(nodes, storage_context=storage_context)\n",
" \n",
" memory = ChatMemoryBuffer.from_defaults(token_limit=15000)\n",
" hi_content_engine = index.as_query_engine(\n",
" memory=memory,\n",
" system_prompt=system_content,\n",
" similarity_top_k=20,\n",
" streaming=True\n",
" )\n",
" hi_textbook_query_description = \"\"\"\n",
" Use this tool to extract content from textbook `Health Insurance 7th Edition`,\n",
" that has 15 chapters in total. When user wants to learn more about a \n",
" particular chapter, this tool will help to assist user to get better\n",
" understanding of the content of the textbook.\n",
" \"\"\"\n",
" \n",
" hi_query_tool = QueryEngineTool.from_defaults(\n",
" query_engine=hi_content_engine,\n",
" name=\"health_insurance_textbook_query_engine\",\n",
" description=hi_textbook_query_description\n",
" )\n",
"\n",
" agent = OpenAIAgent.from_tools(tools=[\n",
" hi_query_tool, \n",
" get_qna_question_tool,\n",
" evaluate_qna_answer_tool\n",
" ],\n",
" max_function_calls=1,\n",
" llm=llm, \n",
" verbose=True,\n",
" system_prompt=textbook_content)\n",
" print(\"loaded AI agent, let's begin the chat!\")\n",
" print(\"=\"*50)\n",
" print(\"\")\n",
"\n",
" return agent\n",
"\n",
"def generate_llm_response(prompt_input, tool_choice=\"auto\"):\n",
" chat_agent = get_query_engine(input_files=input_files, \n",
" llm_model=selected_model, \n",
" temperature=temperature,\n",
" embedding_model=embedding_model,\n",
" fine_tuned_path=fine_tuned_path,\n",
" system_content=system_content,\n",
" persisted_vector_db=persisted_vector_db)\n",
" \n",
" # st.session_state.messages\n",
" response = chat_agent.stream_chat(prompt_input, tool_choice=tool_choice)\n",
" return response\n",
"\n",
"def handle_feedback(user_response):\n",
" st.toast(\"✔️ Feedback received!\")\n",
" st.session_state.feedback = False\n",
"\n",
"def handle_image_upload():\n",
" st.session_state.release_file = \"true\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f148426b-1634-45ed-a1fa-44e9c6ab14ac",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4461d081-d8d0-4801-ad52-dbe826cbfe59",
"metadata": {},
"outputs": [],
"source": [
"openai_api = os.getenv(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a24c861-896b-4800-8478-73f8cd65e8fa",
"metadata": {},
"outputs": [],
"source": [
"image_prompt = False\n",
"# llm_model = \"gpt-3.5-turbo-0125\"\n",
"llm_model = \"gpt-4-0125-preview\"\n",
"temperature = 0\n",
"\n",
"input_files = [\"./raw_documents/HI Chapter Summary Version 1.3.pdf\",\n",
" \"./raw_documents/qna.txt\"]\n",
"embedding_model = \"BAAI/bge-small-en-v1.5\"\n",
"persisted_vector_db = \"../models/chroma_db\"\n",
"fine_tuned_path = \"local:../models/fine-tuned-embeddings\"\n",
"system_content = (\n",
" \"You are a helpful study assistant. \"\n",
" \"You do not respond as 'User' or pretend to be 'User'. \"\n",
" \"You only respond once as 'Assistant'.\"\n",
")\n",
"textbook_content = (\n",
" \"The content of the textbook `Health Insurance 7th Edition` are as follows,\"\n",
" \"- Chapter 1: Overview Of Healthcare Environment In Singapore\"\n",
" \"- Chapter 2: Medical Expense Insurance\"\n",
" \"- Chapter 3: Group Medical Expense Insurance\"\n",
" \"- Chapter 4: Disability Income Insurance\"\n",
" \"- Chapter 5: Long-Term Care Insurance \"\n",
" \"- Chapter 6: Critical Illness Insurance\"\n",
" \"- Chapter 7: Other Types Of Health Insurance\"\n",
" \"- Chapter 8: Managed Healthcare\"\n",
" \"- Chapter 9: Part I Healthcare Financing\"\n",
" \"- Chapter 9: Part II Healthcare Financing\"\n",
" \"- Chapter 10: Common Policy Provisions\"\n",
" \"- Chapter 11: Health Insurance Pricing\"\n",
" \"- Chapter 12: Health Insurance Underwriting\"\n",
" \"- Chapter 13: Notice No: MAS 120 Disclosure And Advisory Process - Requirements For Accident And Health Insurance Products\"\n",
" \"- Chapter 14: Financial Needs Analysis\"\n",
" \"- Chapter 15: Case Studies\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5e4b22c-1e29-4ab8-9039-6e86f566871a",
"metadata": {},
"outputs": [],
"source": [
"llm = get_llm_object(llm_model, temperature)\n",
"embedded_model = get_embedding_model(\n",
" model_name=embedding_model, \n",
" fine_tuned_path=fine_tuned_path\n",
")\n",
"Settings.llm = llm\n",
"Settings.chunk_size = 1024\n",
"Settings.embed_model = embedded_model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e92d21e3-8483-4f24-91cf-40a6c10d43c5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5753c6ed-41a6-40b5-bc4f-477eb7c1d5c5",
"metadata": {},
"outputs": [],
"source": [
"print(\"loading from vector database - chroma\")\n",
"db = chromadb.PersistentClient(path=persisted_vector_db)\n",
"chroma_collection = db.get_or_create_collection(\"quickstart\")\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"\n",
"index = VectorStoreIndex.from_vector_store(\n",
" vector_store=vector_store,\n",
" storage_context=storage_context\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d91e2dda-cb74-4d85-adce-a4a72c53cc7d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4211bb2-aba9-4be2-b2f1-6fbd3f7e4223",
"metadata": {},
"outputs": [],
"source": [
"memory = ChatMemoryBuffer.from_defaults(token_limit=15000)\n",
"hi_content_engine = index.as_query_engine(\n",
" memory=memory,\n",
" system_prompt=system_content,\n",
" similarity_top_k=8,\n",
" verbose=True,\n",
" streaming=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "007f8bf5-19c5-4462-b5f2-5f4ff30f593b",
"metadata": {},
"outputs": [],
"source": [
"hi_textbook_query_description = \"\"\"\n",
" Use this tool to extract content from the query engine,\n",
" which is built by ingesting textbook content from `Health Insurance 7th Edition`,\n",
" that has 15 chapters in total. When user wants to learn more about a \n",
" particular chapter, this tool will help to assist user to get better\n",
" understanding of the content of the textbook.\n",
"\"\"\"\n",
"\n",
"hi_query_tool = QueryEngineTool.from_defaults(\n",
" query_engine=hi_content_engine,\n",
" name=\"health_insurance_textbook_query_engine\",\n",
" description=hi_textbook_query_description\n",
")\n",
"agent = OpenAIAgent.from_tools(tools=[\n",
" hi_query_tool, \n",
" get_qna_question_tool,\n",
" evaluate_qna_answer_tool\n",
" ],\n",
" max_function_calls=1,\n",
" llm=llm, \n",
" verbose=True,\n",
" system_prompt=textbook_content)\n",
"\n",
"print(\"loaded AI agent, let's begin the chat!\")\n",
"print(\"=\"*50)\n",
"print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2e42ad6-20fc-4f2e-a4ea-403e79b14ba4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c62e817e-c7c8-4f90-9e32-217fec376565",
"metadata": {},
"outputs": [],
"source": [
"response = hi_content_engine.query(\"can you give me the list of chapters that `Health Insurance 7th Edition` covers\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5902ffd2-2f66-4b89-bf7f-a05e3fdeccaa",
"metadata": {},
"outputs": [],
"source": [
"for res in response.response_gen:\n",
" print(res, end=\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e75453b-85c7-4e1c-8683-6df45a13cacb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b97d90d-5c59-486f-863b-4aaa12ed0ea0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4584aa46-b488-4535-9d69-2736c9dad170",
"metadata": {},
"outputs": [],
"source": [
"response = agent.stream_chat(\"hihi\", tool_choice=\"auto\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eff8bb8d-a2d1-428a-9c3d-193389378288",
"metadata": {},
"outputs": [],
"source": [
"for res in response.response_gen:\n",
" print(res, end=\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7a504af-6499-4649-8e68-2a86d415e458",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
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},
"nbformat": 4,
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}
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