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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core import SimpleDirectoryReader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"passbook_id = \"user001\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Document(id_='2612cab8-acbb-48c8-87a7-839b628cf654', embedding=None, metadata={'file_path': 'passbooks\\\\user001.json', 'file_name': 'user001.json', 'file_type': 'application/json', 'file_size': 1878, 'creation_date': '2024-05-05', 'last_modified_date': '2024-05-06'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='[\\r\\n {\\r\\n \"entry_id\": 1,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Groceries\",\\r\\n \"debit\": 100.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 25640.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 2,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Salary\",\\r\\n \"debit\": null,\\r\\n \"credit\": 1000.00,\\r\\n \"balance\": 26640.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 3,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Rent\",\\r\\n \"debit\": 500.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 26140.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 4,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Electricity\",\\r\\n \"debit\": 200.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 25940.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 5,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Internet\",\\r\\n \"debit\": 50.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 25890.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 6,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Salary\",\\r\\n \"debit\": null,\\r\\n \"credit\": 1000.00,\\r\\n \"balance\": 26890.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 7,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Groceries\",\\r\\n \"debit\": 100.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 26790.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 8,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Rent\",\\r\\n \"debit\": 500.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 26290.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 9,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Electricity\",\\r\\n \"debit\": 200.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 26090.00\\r\\n },\\r\\n\\r\\n {\\r\\n \"entry_id\": 10,\\r\\n \"date\": \"06-05-2024\",\\r\\n \"description\": \"Internet\",\\r\\n \"debit\": 50.00,\\r\\n \"credit\": null,\\r\\n \"balance\": 26040.00\\r\\n }\\r\\n]', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]\n"
]
}
],
"source": [
"json_file = SimpleDirectoryReader(\n",
" input_files = [f\"passbooks/{passbook_id}.json\"]\n",
" ).load_data()\n",
"print(json_file)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core.prompts.prompts import SimpleInputPrompt\n",
"\n",
"system_prompt=\"\"\"\n",
"You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided.\n",
"You will be provided with list of bank statements and you have to analyse the debits and credits based on the description and date and answer accordingly to user queries.\n",
"\"\"\"\n",
"\n",
"query_wrapper_prompt=SimpleInputPrompt(\"<|USER|>{query_str}<|ASSISTANT|>\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ac9bb85d9e1454cab179dd35d68acc9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"login()\n",
"from huggingface_hub import login"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67442839d6c2476ead1161d68bc1aeb8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors.index.json: 0%| | 0.00/26.8k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4247816f85c4b739086ee4f9d931524",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e7d41810429e4265974a3fd1f1b08163",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model-00001-of-00002.safetensors: 0%| | 0.00/9.98G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d50b062af094dbe9b9e533f774afdad",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model-00002-of-00002.safetensors: 0%| | 0.00/3.50G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84ff3eea97f940819f3faad537d6099d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e95f5b3a53c3415c86e47f4cdbec05ab",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"generation_config.json: 0%| | 0.00/188 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu and disk.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26503efe392d46dca3e2553747db4c13",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/1.62k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dd746f7ec6674bb2afad573344f8b95c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.model: 0%| | 0.00/500k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4e2ffaa78024eadb753ea3d87b12489",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0%| | 0.00/1.84M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "01c4aaaede0b40b28946a4bef395d0c6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from llama_index.llms.huggingface import HuggingFaceLLM\n",
"import torch\n",
"\n",
"\n",
"llm = HuggingFaceLLM(\n",
" context_window=4096,\n",
" max_new_tokens=256,\n",
" generate_kwargs={\"temperature\": 0.0, \"do_sample\": False},\n",
" system_prompt=system_prompt,\n",
" query_wrapper_prompt=query_wrapper_prompt,\n",
" tokenizer_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
" model_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
" device_map=\"auto\",\n",
" model_kwargs={\"torch_dtype\": torch.float16},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
"from llama_index.legacy.embeddings.langchain import LangchainEmbedding\n",
"\n",
"embed_model=LangchainEmbedding(\n",
" HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\puran\\AppData\\Local\\Temp\\ipykernel_4904\\1600445012.py:2: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
" service_context = ServiceContext.from_defaults(\n"
]
}
],
"source": [
"from llama_index.core import ServiceContext\n",
"service_context = ServiceContext.from_defaults(\n",
" chunk_size=1024,\n",
" llm=llm,\n",
" embed_model=embed_model\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core import VectorStoreIndex\n",
"\n",
"vector_index = VectorStoreIndex.from_documents(json_file, service_context=service_context)\n",
"query_engine = vector_index.as_query_engine()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\puran\\OneDrive\\Desktop\\RAG_Testing\\rag\\lib\\site-packages\\transformers\\generation\\configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
" warnings.warn(\n",
"c:\\Users\\puran\\OneDrive\\Desktop\\RAG_Testing\\rag\\lib\\site-packages\\transformers\\generation\\configuration_utils.py:497: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
" warnings.warn(\n"
]
}
],
"source": [
"response=query_engine.query(\"where do i debit frequently?\")\n",
"print(response)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "rag",
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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