Spaces:
Build error
Build error
File size: 6,497 Bytes
db7706f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import PyMuPDFLoader\n",
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"from langchain.retrievers import ParentDocumentRetriever\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
"from langchain_community.document_loaders.csv_loader import CSVLoader\n",
"import chromadb\n",
"from chromadb.utils import embedding_functions\n",
"import os\n",
"\n",
"# Reference : https://towardsdatascience.com/rag-how-to-talk-to-your-data-eaf5469b83b0\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kishoregajjala/anaconda3/envs/mhc_1/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": [
"# create the open-source embedding function\n",
"huggingface_ef = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"persist_directory=\"Data/chroma\"\n",
"chroma_client = chromadb.PersistentClient(path=persist_directory)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever\n",
"parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)\n",
"\n",
"# This text splitter is used to create the child documents\n",
"# It should create documents smaller than the parent\n",
"child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def get_file_paths_recursively(folder_path):\n",
" file_paths = []\n",
" for root, directories, files in os.walk(folder_path):\n",
" for file in files:\n",
" file_path = os.path.join(root, file)\n",
" file_paths.append(file_path)\n",
" return file_paths\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def vdb_csv_loader(file_paths):\n",
" for i in range(len(file_paths)):\n",
" loader = CSVLoader(file_path=file_paths[i], encoding=\"latin-1\")\n",
" db = Chroma.from_documents(documents=loader.load(), embedding=huggingface_ef, collection_name= \"mental_health_csv_collection\", persist_directory=persist_directory) # pars to imclude (docs, emb_fun, col_name, direct_path)\n",
"\n",
"###\n",
"def generate_csv_vector_db() -> None:\n",
" \n",
" # Get the directory path of the current script\n",
" #script_dir = os.path.dirname(os.path.abspath(__file__))\n",
" folder_path = \"Data/csv\"\n",
" file_paths = get_file_paths_recursively(folder_path)\n",
"\n",
" #loaded all the files\n",
" vdb_csv_loader(file_paths)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"pdf_collection = Chroma(collection_name=\"mental_health_pdf_collection\", embedding_function=huggingface_ef, persist_directory=persist_directory) \n",
"def vdb_pdf_loader(file_paths):\n",
" for i in range(len(file_paths)):\n",
" loader = PyMuPDFLoader(file_path=file_paths[i])\n",
" documents = loader.load()\n",
" \n",
" store = InMemoryStore()\n",
" rag_retriever = ParentDocumentRetriever(\n",
" vectorstore=pdf_collection,\n",
" docstore=store,\n",
" child_splitter=child_splitter,\n",
" parent_splitter=parent_splitter,\n",
" )\n",
" rag_retriever.add_documents(documents)\n",
"\n",
"\n",
"def generate_pdf_vector_db() -> None:\n",
" \n",
" # Get the directory path of the current script\n",
" #script_dir = os.path.dirname(os.path.abspath(__file__))\n",
" folder_path = \"Data/pdf\" #os.path.join(script_dir, '/Data/pdf') \n",
" file_paths = get_file_paths_recursively(folder_path)\n",
" vdb_pdf_loader(file_paths)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" # call PDF loader\n",
"generate_pdf_vector_db()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# call csv loader\n",
"generate_csv_vector_db()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"def vectordb_load(): \n",
" # call csv loader\n",
" generate_csv_vector_db()\n",
"\n",
" # call PDF loader\n",
" generate_pdf_vector_db()\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# call vector db load\n",
"vectordb_load()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "mhc_1",
"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": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|