{ "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", "from prompt_engineering import (\n", " system_content, \n", " textbook_content, \n", " winnie_the_pooh_prompt, \n", " introduction_line\n", ")\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=100_000)\n", " hi_content_engine = index.as_query_engine(\n", " memory=memory,\n", " system_prompt=system_content,\n", " similarity_top_k=10,\n", " verbose=True,\n", " streaming=True\n", " )\n", " 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", "\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_advanced_corrected\"\n", "\n", "# fine_tuned_path = \"local:../models/fine-tuned-embeddings\"\n", "fine_tuned_path = \"local:../models/fine-tuned-embeddings-advanced\"" ] }, { "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": "a49ed30e-a631-4618-a79e-adab02114d8d", "metadata": {}, "outputs": [], "source": [] }, { "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": "66c8881d-fc57-4e95-ad86-110c4818e2fe", "metadata": {}, "outputs": [], "source": [ "# query_string = \"tell me more about integrated shield plans\"\n", "query_string = \"how to use CPF\"\n", "# query_string = \"what is MediSave\"" ] }, { "cell_type": "code", "execution_count": null, "id": "9cbd338b-bee5-4c06-9934-a0e27fd518d3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5902ffd2-2f66-4b89-bf7f-a05e3fdeccaa", "metadata": {}, "outputs": [], "source": [ "response = hi_content_engine.query(query_string)\n", "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": "4584aa46-b488-4535-9d69-2736c9dad170", "metadata": {}, "outputs": [], "source": [ "response = agent.stream_chat(query_string, tool_choice=\"auto\")\n", "for res in response.response_gen:\n", " print(res, end=\"\")" ] }, { "cell_type": "code", "execution_count": null, "id": "eff8bb8d-a2d1-428a-9c3d-193389378288", "metadata": {}, "outputs": [], "source": [] }, { "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" } }, "nbformat": 4, "nbformat_minor": 5 }