rag.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76379cff-da1a-4ae2-a5fa-a8ac8ff81628",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.llms import HuggingFacePipeline, LlamaCpp\n",
    "from langchain.chains import RetrievalQA\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.callbacks.manager import CallbackManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f170b6d-20c0-47e7-89c4-181bafc1a0cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/prateek.durgapal/miniconda3/envs/edubot/lib/python3.11/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",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "raw_documents = TextLoader(\"./chapters.txt\").load()\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=500,\n",
    "    chunk_overlap=50,\n",
    "    length_function=len,\n",
    "    is_separator_regex=False,\n",
    ")\n",
    "documents = text_splitter.split_documents(raw_documents)\n",
    "db = Chroma.from_documents(documents, HuggingFaceEmbeddings())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ef46c8f-a722-4f6b-ab0b-f26257eeb7ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we discussed this earlier in the index funds chapter as well. in the last decade or so, index funds and etfs have become increasingly popular around the world. one reason is that investors have increasingly realized that a vast majority of actively managed mutual funds don’t beat their benchmarks. in a developed market like the us, ~90% of active funds don’t beat their benchmarks.\n"
     ]
    }
   ],
   "source": [
    "query = \"What are index funds? Describe in one line.\"\n",
    "docs = db.similarity_search(query)\n",
    "print(docs[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77374ad4-0fc9-4e9e-bba8-a3b11ade1b27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "llama_model_loader: - tensor  142:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  143:           blk.15.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  144:           blk.15.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  145:             blk.15.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  146:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  147:             blk.16.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  148:             blk.16.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  149:             blk.16.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  150:        blk.16.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  151:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  152:           blk.16.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  153:           blk.16.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  154:             blk.16.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  155:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  156:             blk.17.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  157:             blk.17.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  158:             blk.17.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  159:        blk.17.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  160:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  161:           blk.17.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  162:           blk.17.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  163:             blk.17.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  164:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  165:             blk.18.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  166:             blk.18.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  167:             blk.18.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  168:        blk.18.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  169:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  170:           blk.18.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  171:           blk.18.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  172:             blk.18.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  173:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  174:             blk.19.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  175:             blk.19.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  176:             blk.19.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  177:        blk.19.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  178:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  179:           blk.19.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  180:           blk.19.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  181:             blk.19.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  182:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  183:             blk.20.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  184:             blk.20.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  185:             blk.20.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  186:        blk.20.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  187:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  188:           blk.20.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  189:           blk.20.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  190:             blk.20.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  191:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  192:             blk.21.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  193:             blk.21.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  194:             blk.21.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  195:        blk.21.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  196:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  197:           blk.21.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  198:           blk.21.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  199:             blk.21.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  200:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  201:             blk.22.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  202:             blk.22.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  203:             blk.22.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  204:        blk.22.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  205:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  206:           blk.22.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  207:           blk.22.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  208:             blk.22.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  209:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  210:             blk.23.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  211:             blk.23.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  212:             blk.23.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  213:        blk.23.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  214:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  215:           blk.23.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  216:           blk.23.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  217:             blk.23.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  218:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  219:             blk.24.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  220:             blk.24.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  221:             blk.24.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  222:        blk.24.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  223:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  224:           blk.24.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  225:           blk.24.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  226:             blk.24.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  227:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  228:             blk.25.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  229:             blk.25.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  230:             blk.25.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  231:        blk.25.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  232:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  233:           blk.25.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  234:           blk.25.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  235:             blk.25.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  236:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  237:             blk.26.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  238:             blk.26.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  239:             blk.26.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  240:        blk.26.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  241:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  242:           blk.26.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  243:           blk.26.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  244:             blk.26.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  245:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  246:             blk.27.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  247:             blk.27.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  248:             blk.27.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  249:        blk.27.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  250:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  251:           blk.27.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  252:           blk.27.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  253:             blk.27.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  254:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  255:             blk.28.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  256:             blk.28.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  257:             blk.28.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  258:        blk.28.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  259:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  260:           blk.28.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  261:           blk.28.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  262:             blk.28.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  263:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  264:             blk.29.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  265:             blk.29.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  266:             blk.29.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  267:        blk.29.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  268:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  269:           blk.29.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  270:           blk.29.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  271:             blk.29.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  272:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  273:             blk.30.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  274:             blk.30.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  275:             blk.30.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  276:        blk.30.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  277:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  278:           blk.30.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  279:           blk.30.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  280:             blk.30.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  281:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  282:             blk.31.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  283:             blk.31.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  284:             blk.31.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  285:        blk.31.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  286:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  287:           blk.31.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  288:           blk.31.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  289:             blk.31.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  290:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - kv   0:                       general.architecture str     \n",
      "llama_model_loader: - kv   1:                               general.name str     \n",
      "llama_model_loader: - kv   2:                        general.description str     \n",
      "llama_model_loader: - kv   3:                       llama.context_length u32     \n",
      "llama_model_loader: - kv   4:                     llama.embedding_length u32     \n",
      "llama_model_loader: - kv   5:                          llama.block_count u32     \n",
      "llama_model_loader: - kv   6:                  llama.feed_forward_length u32     \n",
      "llama_model_loader: - kv   7:                 llama.rope.dimension_count u32     \n",
      "llama_model_loader: - kv   8:                 llama.attention.head_count u32     \n",
      "llama_model_loader: - kv   9:              llama.attention.head_count_kv u32     \n",
      "llama_model_loader: - kv  10:     llama.attention.layer_norm_rms_epsilon f32     \n",
      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str     \n",
      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr     \n",
      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr     \n",
      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr     \n",
      "llama_model_loader: - kv  15:            tokenizer.ggml.unknown_token_id u32     \n",
      "llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32     \n",
      "llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32     \n",
      "llama_model_loader: - type  f32:   65 tensors\n",
      "llama_model_loader: - type q4_0:  226 tensors\n",
      "llm_load_print_meta: format           = GGUF V2 (latest)\n",
      "llm_load_print_meta: arch             = llama\n",
      "llm_load_print_meta: vocab type       = SPM\n",
      "llm_load_print_meta: n_vocab          = 32000\n",
      "llm_load_print_meta: n_merges         = 0\n",
      "llm_load_print_meta: n_ctx_train      = 2048\n",
      "llm_load_print_meta: n_embd           = 4096\n",
      "llm_load_print_meta: n_head           = 32\n",
      "llm_load_print_meta: n_head_kv        = 32\n",
      "llm_load_print_meta: n_layer          = 32\n",
      "llm_load_print_meta: n_rot            = 128\n",
      "llm_load_print_meta: n_gqa            = 1\n",
      "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
      "llm_load_print_meta: f_norm_rms_eps   = 1.0e-05\n",
      "llm_load_print_meta: n_ff             = 11008\n",
      "llm_load_print_meta: freq_base_train  = 10000.0\n",
      "llm_load_print_meta: freq_scale_train = 1\n",
      "llm_load_print_meta: model type       = 7B\n",
      "llm_load_print_meta: model ftype      = mostly Q4_0 (guessed)\n",
      "llm_load_print_meta: model params     = 6.74 B\n",
      "llm_load_print_meta: model size       = 3.53 GiB (4.50 BPW) \n",
      "llm_load_print_meta: general.name   = llama-2-7b-chat.ggmlv3.q4_0.bin\n",
      "llm_load_print_meta: BOS token = 1 '<s>'\n",
      "llm_load_print_meta: EOS token = 2 '</s>'\n",
      "llm_load_print_meta: UNK token = 0 '<unk>'\n",
      "llm_load_print_meta: LF token  = 13 '<0x0A>'\n",
      "llm_load_tensors: ggml ctx size =    0.09 MB\n",
      "llm_load_tensors: mem required  = 3615.73 MB\n",
      "...................................................................................................\n",
      "llama_new_context_with_model: n_ctx      = 1024\n",
      "llama_new_context_with_model: freq_base  = 10000.0\n",
      "llama_new_context_with_model: freq_scale = 1\n",
      "llama_new_context_with_model: kv self size  =  512.00 MB\n",
      "llama_new_context_with_model: compute buffer total size = 7.28 MB\n",
      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n"
     ]
    }
   ],
   "source": [
    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
    "llm = LlamaCpp(\n",
    "    model_path=\"./llama2/7B-chat-gguf/llama-2-7b-chat.ggufv3.q4_0.bin\",\n",
    "    temperature=0.0,\n",
    "    max_tokens=128,\n",
    "    top_p=0.1,\n",
    "    n_ctx=1024,\n",
    "    callback_manager=callback_manager,\n",
    "    verbose=True, # verbose is required to pass to the callback manager\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e48c0177-9c99-46b9-a7e2-ce886e8565f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Unterscheidung between active and passive management.\n",
      "Index funds are a type of mutual fund or exchange-traded fund (ETF) that tracks a particular market index, such as the S&P 500 or the Dow Jones Industrial Average, by holding a portfolio of securities that mirrors the performance of the index. In contrast to actively managed funds, which aim to outperform the market through research and analysis of individual securities, index funds do not attempt to beat the market but rather match its performance."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =  4973.59 ms\n",
      "llama_print_timings:      sample time =    71.09 ms /   113 runs   (    0.63 ms per token,  1589.56 tokens per second)\n",
      "llama_print_timings: prompt eval time =  5423.05 ms /    12 tokens (  451.92 ms per token,     2.21 tokens per second)\n",
      "llama_print_timings:        eval time = 16560.40 ms /   112 runs   (  147.86 ms per token,     6.76 tokens per second)\n",
      "llama_print_timings:       total time = 22411.73 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Unterscheidung between active and passive management.\\nIndex funds are a type of mutual fund or exchange-traded fund (ETF) that tracks a particular market index, such as the S&P 500 or the Dow Jones Industrial Average, by holding a portfolio of securities that mirrors the performance of the index. In contrast to actively managed funds, which aim to outperform the market through research and analysis of individual securities, index funds do not attempt to beat the market but rather match its performance.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3389ee9-1264-49f2-aebd-6da3862b725a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rag_pipeline = RetrievalQA.from_chain_type(\n",
    "    llm=llm, chain_type=\"stuff\",\n",
    "    retriever=db.as_retriever(),\n",
    "    return_source_documents=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7049f36d-c1ce-46a5-822b-023f6f80788b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    }
   ],
   "source": [
    "output = rag_pipeline(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7f83f18-2253-4b43-b727-540dd498fc16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source Document 1:\n",
      "we discussed this earlier in the index funds chapter as well. in the last decade or so, index funds and etfs have become increasingly popular around the world. one reason is that investors have increasingly realized that a vast majority of actively managed mutual funds don’t beat their benchmarks. in a developed market like the us, ~90% of active funds don’t beat their benchmarks.\n",
      "\n",
      "Source Document 2:\n",
      "indexation is a simple method to determine the true value of the sale of an asset after considering the effect of inflation. this can be done with the help of the cost inflation index (cii) which can be found on the income tax website.\n",
      "let me explain this with an example of a purchase/sale of a debt mutual fund.\n",
      "purchase value: rs.100,000/-\n",
      "year of purchase: 2005\n",
      "sale value: rs 300,000\n",
      "year of sale: 2015\n",
      "long term capital gain: rs 200,000/-\n",
      "\n",
      "Source Document 3:\n",
      "- an index acts as a barometer of the whole economy.\n",
      "- an index going up indicates that the market participants are optimistic.\n",
      "- an index going down indicates that the market participants are pessimistic.\n",
      "- there are two main indices in india – the bse sensex and nse’s nifty\n",
      "- an index can be used for a variety of purposes – information, benchmarking, trading and hedging.\n",
      "- index trading is probably the most popular use of the index.\n",
      "\n",
      "Source Document 4:\n",
      "29.9 – etf vs index funds\n",
      "this is another question that keeps coming up. hopefully, this table should help answer these questions. in short, if you want to have active control or if you are actively using passive etfs, then etfs are a better choice. but if you are lazy like me and want to make as fewer choices as possible, then index funds are a better choice.\n",
      "with etfs, you can express tactical strategies better than index funds because you can’t buy and sell index funds immediately.\n",
      "|\n",
      "|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, doc in enumerate(output[\"source_documents\"]):\n",
    "    print(f\"Source Document {i + 1}:\")\n",
    "    print(doc.page_content)\n",
    "    print()"
   ]
  }
 ],
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