928 lines
27 KiB
Plaintext
928 lines
27 KiB
Plaintext
{
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"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "fOxyiqtzKqLg",
|
||
"outputId": "714d12c5-775e-42c8-b51c-979a9112b808"
|
||
},
|
||
"outputs": [],
|
||
"source": [
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"!pip install -q datasets requests torch peft bitsandbytes transformers trl accelerate sentencepiece tiktoken matplotlib gradio modal ollama langchain langchain-core langchain-text-splitters langchain-openai langchain-chroma langchain-community faiss-cpu feedparser"
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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": null,
|
||
"metadata": {
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||
"id": "zyxwwUw6LWXK"
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},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import glob\n",
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"from dotenv import load_dotenv\n",
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"import gradio as gr"
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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": null,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
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||
"id": "Zzqc9nk1L_5w",
|
||
"outputId": "0af5e1bb-2ccb-4838-b7a5-76c19285d094"
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import DirectoryLoader, TextLoader, UnstructuredPDFLoader\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.schema import Document\n",
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"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
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"from langchain_chroma import Chroma\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.manifold import TSNE\n",
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"import numpy as np\n",
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"import plotly.graph_objects as go\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain.chains import ConversationalRetrievalChain\n",
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"from langchain.embeddings import HuggingFaceEmbeddings\n",
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"from huggingface_hub import login\n",
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"import torch\n",
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"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, set_seed\n",
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"from google.colab import userdata\n",
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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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": null,
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"metadata": {
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"id": "u_vbe1itNZ2n"
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},
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"outputs": [],
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"source": [
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"base_path = \"/content/drive/MyDrive/sameer-db\"\n",
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"folders = glob.glob(os.path.join(base_path, \"*\"))"
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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": null,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
|
||
"id": "f0lJBMjhMrLO",
|
||
"outputId": "5cdc6327-3a3a-4d5b-ca05-4c1383c020e2"
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||
},
|
||
"outputs": [],
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"source": [
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"def add_metadata(doc, doc_type):\n",
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" doc.metadata[\"doc_type\"] = doc_type\n",
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" return doc\n",
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"\n",
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"# With thanks to CG and Jon R, students on the course, for this fix needed for some users\n",
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"text_loader_kwargs = {'encoding': 'utf-8'}\n",
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"# If that doesn't work, some Windows users might need to uncomment the next line instead\n",
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"# text_loader_kwargs={'autodetect_encoding': True}\n",
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"\n",
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"documents = []\n",
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"for folder in folders:\n",
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" doc_type = os.path.basename(folder)\n",
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" loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
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" folder_docs = loader.load()\n",
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" documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n",
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"\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
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"chunks = text_splitter.split_documents(documents)\n",
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"\n",
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"print(f\"Total number of chunks: {len(chunks)}\")\n",
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"print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")"
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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": null,
|
||
"metadata": {
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||
"id": "zSjwqZ3YNBLp"
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"hf_token = userdata.get('HF_TOKEN')\n",
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"login(hf_token, add_to_git_credential=True)"
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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": null,
|
||
"metadata": {
|
||
"id": "t7rraUyHNkdP"
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||
},
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||
"outputs": [],
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||
"source": [
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||
"Phi_4 = \"microsoft/Phi-4-mini-instruct\"\n",
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"db_name = \"/content/drive/MyDrive/phi_vector_db\""
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||
]
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},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
|
||
"id": "pDjj2S5ZPzF1"
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||
},
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||
"outputs": [],
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||
"source": [
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"quant_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_use_double_quant=True,\n",
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||
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
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" bnb_4bit_quant_type=\"nf4\"\n",
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||
" )"
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]
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},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 66,
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||
"referenced_widgets": [
|
||
"2a0377fc1e0c4c08944be1857c4e2409",
|
||
"7c8335e0c3f8459d89f3b9815a896e39",
|
||
"0fcb91f0551a4871b747f82e5fa6ff38",
|
||
"fa5c6cf8395840e08e2743d6e88190be",
|
||
"8613224ada934e7ba57fd5184ea61044",
|
||
"1180c8fe49e94873a024d38d33649852",
|
||
"4395c417cc854fc48da18d0ddd62671e",
|
||
"d678106a6601478cb5712991604788f0",
|
||
"5c4a8d25dbc942d5a596c8fa8580a785",
|
||
"c1b076c063e04536831d68e5e48f1692",
|
||
"9bcee7f185434cd0b1a998448236548c"
|
||
]
|
||
},
|
||
"id": "qzQzgir5VUBF",
|
||
"outputId": "1e7198a3-4857-49ab-f368-d430beddbf42"
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||
},
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||
"outputs": [],
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||
"source": [
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||
"tokenizer = AutoTokenizer.from_pretrained(Phi_4, trust_remote_code=True)\n",
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"tokenizer.pad_token = tokenizer.eos_token\n",
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"tokenizer.padding_side = \"right\"\n",
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"\n",
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"base_model = AutoModelForCausalLM.from_pretrained(\n",
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" Phi_4,\n",
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||
" quantization_config=quant_config,\n",
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||
" device_map=\"auto\",\n",
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")\n",
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"base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n",
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"\n",
|
||
"print(f\"Memory footprint: {base_model.get_memory_footprint() / 1e9:.1f} GB\")"
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]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "MjK3mBKHQBra"
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||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.embeddings.base import Embeddings\n",
|
||
"from typing import List\n",
|
||
"import torch.nn.functional as F"
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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": null,
|
||
"metadata": {
|
||
"id": "Q1BIMVW4Pf0A"
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||
},
|
||
"outputs": [],
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||
"source": [
|
||
"class PHI4Embeddings(Embeddings):\n",
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" def __init__(self, tokenizer, model):\n",
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" self.tokenizer = tokenizer\n",
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" self.model = model\n",
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||
" self.model.eval()\n",
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"\n",
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" def embed_documents(self, texts: List[str]) -> List[List[float]]:\n",
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" embeddings = []\n",
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" for text in texts:\n",
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" with torch.no_grad():\n",
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" inputs = self.tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=512).to(self.model.device)\n",
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" outputs = self.model(**inputs, output_hidden_states=True)\n",
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" hidden_states = outputs.hidden_states[-1] # Last layer\n",
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" attention_mask = inputs[\"attention_mask\"].unsqueeze(-1)\n",
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" pooled = (hidden_states * attention_mask).sum(dim=1) / attention_mask.sum(dim=1)\n",
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||
" normalized = F.normalize(pooled, p=2, dim=1)\n",
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" embeddings.append(normalized[0].cpu().tolist())\n",
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" return embeddings\n",
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"\n",
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||
" def embed_query(self, text: str) -> List[float]:\n",
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||
" return self.embed_documents([text])[0]"
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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": null,
|
||
"metadata": {
|
||
"id": "7aUTue_mMxof"
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||
},
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||
"outputs": [],
|
||
"source": [
|
||
"# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n",
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"\n",
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||
"embeddings = PHI4Embeddings(tokenizer, base_model)\n",
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"\n",
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||
"# Delete if already exists\n",
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||
"\n",
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||
"if os.path.exists(db_name):\n",
|
||
" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "uWSe-8mATUag",
|
||
"outputId": "296804af-2283-435a-908c-48adaa6b4fd9"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Create vectorstore\n",
|
||
"vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
|
||
"print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "1ZQ6agxtSLp5",
|
||
"outputId": "8e5bf8a7-fbaf-427b-9a67-369945aba80e"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Let's investigate the vectors\n",
|
||
"\n",
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||
"collection = vectorstore._collection\n",
|
||
"count = collection.count()\n",
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||
"\n",
|
||
"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
|
||
"dimensions = len(sample_embedding)\n",
|
||
"print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "qBIOPr2YT5FM"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Prework\n",
|
||
"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
|
||
"vectors = np.array(result['embeddings'])\n",
|
||
"documents = result['documents']\n",
|
||
"metadatas = result['metadatas']\n",
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||
"doc_types = [metadata['doc_type'] for metadata in metadatas]\n",
|
||
"colors = [['blue', 'red'][['personal', 'profile'].index(t)] for t in doc_types]"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 617
|
||
},
|
||
"id": "fnuul36bUB3h",
|
||
"outputId": "f6cf1650-910a-4a03-f92d-9c200fb37de7"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# We humans find it easier to visalize things in 2D!\n",
|
||
"# Reduce the dimensionality of the vectors to 2D using t-SNE\n",
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||
"# (t-distributed stochastic neighbor embedding)\n",
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||
"\n",
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||
"tsne = TSNE(n_components=2, random_state=42, perplexity=4)\n",
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||
"reduced_vectors = tsne.fit_transform(vectors)\n",
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"\n",
|
||
"# Create the 2D scatter plot\n",
|
||
"fig = go.Figure(data=[go.Scatter(\n",
|
||
" x=reduced_vectors[:, 0],\n",
|
||
" y=reduced_vectors[:, 1],\n",
|
||
" mode='markers',\n",
|
||
" marker=dict(size=5, color=colors, opacity=0.8),\n",
|
||
" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n",
|
||
" hoverinfo='text'\n",
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")])\n",
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||
"\n",
|
||
"fig.update_layout(\n",
|
||
" title='2D Chroma Vector Store Visualization',\n",
|
||
" scene=dict(xaxis_title='x',yaxis_title='y'),\n",
|
||
" width=800,\n",
|
||
" height=600,\n",
|
||
" margin=dict(r=20, b=10, l=10, t=40)\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.show()"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 717
|
||
},
|
||
"id": "Dgaeb7aRUF5d",
|
||
"outputId": "47546459-e169-4d2b-d0d7-4ebd135556e0"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Let's try 3D!\n",
|
||
"\n",
|
||
"tsne = TSNE(n_components=3, random_state=42, perplexity=4)\n",
|
||
"reduced_vectors = tsne.fit_transform(vectors)\n",
|
||
"\n",
|
||
"# Create the 3D scatter plot\n",
|
||
"fig = go.Figure(data=[go.Scatter3d(\n",
|
||
" x=reduced_vectors[:, 0],\n",
|
||
" y=reduced_vectors[:, 1],\n",
|
||
" z=reduced_vectors[:, 2],\n",
|
||
" mode='markers',\n",
|
||
" marker=dict(size=5, color=colors, opacity=0.8),\n",
|
||
" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n",
|
||
" hoverinfo='text'\n",
|
||
")])\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" title='3D Chroma Vector Store Visualization',\n",
|
||
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
|
||
" width=900,\n",
|
||
" height=700,\n",
|
||
" margin=dict(r=20, b=10, l=10, t=40)\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "BZcCyGI3YEwJ",
|
||
"outputId": "fd03e6ee-2ec1-4c6b-c14b-986255ca070c"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.llms import HuggingFacePipeline\n",
|
||
"from transformers import pipeline\n",
|
||
"\n",
|
||
"pipe = pipeline(\n",
|
||
" \"text-generation\",\n",
|
||
" model=base_model,\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" max_new_tokens=4069,\n",
|
||
" return_full_text=False,\n",
|
||
" temperature=0.7\n",
|
||
")\n",
|
||
"\n",
|
||
"llm = HuggingFacePipeline(pipeline=pipe)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "WDY8-1gJUM1v"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# set up the conversation memory for the chat\n",
|
||
"from langchain.schema import SystemMessage\n",
|
||
"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
|
||
"# memory.chat_memory.add_message(SystemMessage(content='''You are a helpful assistant that answers questions about Sameer Khadatkar **in English only**, based only on the retrieved documents.\n",
|
||
"# Do not respond in any other language.'''))\n",
|
||
"\n",
|
||
"# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
|
||
"retriever = vectorstore.as_retriever(k=2)\n",
|
||
"\n",
|
||
"# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
|
||
"conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "dkuv5wD6jCrX"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def extract_first_helpful_answer(output: str) -> str:\n",
|
||
" if \"Helpful Answer:\" in output:\n",
|
||
" parts = output.split(\"Helpful Answer:\")\n",
|
||
" return parts[0].strip().split(\"\\n\")[0].strip() # Take only the first line after it\n",
|
||
" return output.strip()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "ZY5BH4C3UY1E"
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},
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"outputs": [],
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"query = \"Who is Sameer\"\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": null,
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"metadata": {
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},
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"outputId": "794c4dad-efde-4220-a9bd-50a1ae156229"
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},
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]
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},
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"\n",
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