mirror of
https://github.com/BaranziniLab/KG_RAG.git
synced 2024-06-08 14:12:54 +03:00
289 lines
8.1 KiB
Plaintext
289 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b86c2320-71ed-4223-9df7-0b9281cb652c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.chdir('..')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8e9dc80f-43a6-4d8d-9d99-343bc6515ff8",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/root/anaconda3/envs/kg_rag_test_2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from kg_rag.utility import *\n",
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"from tqdm import tqdm\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3b991006-9e91-4db1-9c11-62cbf1d9c356",
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"metadata": {},
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"source": [
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"## Choose the LLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5ae38918-24e1-4a28-b4e5-461eda38002c",
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"metadata": {},
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"outputs": [],
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"source": [
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"LLM_MODEL = 'gpt-4-32k'\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db3c5056-15d6-4608-87c8-1e897dc4075e",
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"metadata": {},
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"source": [
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"## Configure KG-RAG"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "fdf4d8fd-2265-4237-ba85-06a3efbf8145",
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"metadata": {},
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"outputs": [],
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"source": [
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"SYSTEM_PROMPT = system_prompts[\"KG_RAG_BASED_TEXT_GENERATION\"]\n",
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"CONTEXT_VOLUME = int(config_data[\"CONTEXT_VOLUME\"])\n",
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"QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD = float(config_data[\"QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD\"])\n",
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"QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY = float(config_data[\"QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY\"])\n",
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"VECTOR_DB_PATH = config_data[\"VECTOR_DB_PATH\"]\n",
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"NODE_CONTEXT_PATH = config_data[\"NODE_CONTEXT_PATH\"]\n",
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"SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL = config_data[\"SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL\"]\n",
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"SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL = config_data[\"SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL\"]\n",
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"TEMPERATURE = config_data[\"LLM_TEMPERATURE\"]\n",
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"\n",
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"CHAT_MODEL_ID = LLM_MODEL\n",
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"EDGE_EVIDENCE = True\n",
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"\n",
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"CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID\n",
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"\n",
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"\n",
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"vectorstore = load_chroma(VECTOR_DB_PATH, SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL)\n",
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"embedding_function_for_context_retrieval = load_sentence_transformer(SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL)\n",
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"node_context_df = pd.read_csv(NODE_CONTEXT_PATH)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "547cf664-8b48-4f19-a232-09a5b2fa4ffa",
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"metadata": {},
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"source": [
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"## Load test data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "00fa2491-901e-44ea-8109-2a60b23771ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv('data/rag_comparison_data.csv')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "39c207c9-49be-449b-9b70-a92cdf8095d3",
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"metadata": {},
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"source": [
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"## Function for chat completion with token usage tracking"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "8ca41e38-79fb-4f68-aa16-db1785b6551f",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def chat_completion_with_token_usage(instruction, system_prompt, chat_model_id, chat_deployment_id, temperature):\n",
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" response = openai.ChatCompletion.create(\n",
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" temperature=temperature,\n",
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" deployment_id=chat_deployment_id,\n",
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" model=chat_model_id,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": instruction}\n",
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" ]\n",
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" )\n",
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" return response['choices'][0]['message']['content'], response.usage.total_tokens\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b2bbab7-72f6-414b-bdd0-0eab4ed842f2",
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"metadata": {},
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"source": [
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"## Run on test data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "637671b2-a06c-4fe4-a7a6-855b0ba48fcd",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100it [11:13, 6.74s/it]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 3min 37s, sys: 9.86 s, total: 3min 47s\n",
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"Wall time: 11min 13s\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"\n",
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"kg_rag_answer = []\n",
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"total_tokens_used = []\n",
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"\n",
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"for index, row in tqdm(data.iterrows()):\n",
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" question = row['question']\n",
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" context = retrieve_context(question, vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY, EDGE_EVIDENCE)\n",
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" enriched_prompt = \"Context: \"+ context + \"\\n\" + \"Question: \" + question\n",
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" output, token_usage = chat_completion_with_token_usage(enriched_prompt, SYSTEM_PROMPT, CHAT_MODEL_ID, CHAT_DEPLOYMENT_ID, temperature=TEMPERATURE)\n",
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" kg_rag_answer.append(output)\n",
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" total_tokens_used.append(token_usage)\n",
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" \n",
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"data.loc[:,'kg_rag_answer'] = kg_rag_answer\n",
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"data.loc[:, 'total_tokens_used'] = total_tokens_used\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18e4b72c-c2a5-4b1a-8100-7ad831eb1401",
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"metadata": {},
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"source": [
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"## Run on perturbed test data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "8a042aa2-2366-4d49-a694-efd6d7b4616b",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100it [09:49, 5.90s/it]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 3min 36s, sys: 9.04 s, total: 3min 45s\n",
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"Wall time: 9min 49s\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"\n",
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"kg_rag_answer = []\n",
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"total_tokens_used = []\n",
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"\n",
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"for index, row in tqdm(data.iterrows()):\n",
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" question = row['question']\n",
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" context = retrieve_context(question, vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY, EDGE_EVIDENCE)\n",
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" enriched_prompt = \"Context: \"+ context + \"\\n\" + \"Question: \" + question\n",
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" output, token_usage = chat_completion_with_token_usage(enriched_prompt, SYSTEM_PROMPT, CHAT_MODEL_ID, CHAT_DEPLOYMENT_ID, temperature=TEMPERATURE)\n",
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" kg_rag_answer.append(output)\n",
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" total_tokens_used.append(token_usage)\n",
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" \n",
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"data.loc[:,'kg_rag_answer_perturbed'] = kg_rag_answer\n",
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"data.loc[:, 'total_tokens_used_perturbed'] = total_tokens_used\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c902260-1d9e-4a52-a377-f0c002c91e16",
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"metadata": {},
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"source": [
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"## Save the result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "d510de56-dd39-4742-8a5a-9bb934690d95",
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"metadata": {},
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"outputs": [],
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"source": [
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"save_path = 'data/results'\n",
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"os.makedirs(save_path, exist_ok=True)\n",
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"data.to_csv(os.path.join(save_path, 'kg_rag_output.csv'), index=False)\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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