wehub-resource-sync
364 lines
11 KiB
Plaintext
364 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# RAG using Meta AI Llama-3\n",
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"\n",
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"\n",
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"<img src=\"./resources/rag_architecture.png\" width=800px>"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import nest_asyncio\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, display\n",
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"\n",
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"from llama_index.core import Settings\n",
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"from llama_index.llms.ollama import Ollama\n",
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"from llama_index.core import PromptTemplate\n",
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"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
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"from llama_index.core import VectorStoreIndex, ServiceContext, SimpleDirectoryReader\n",
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"\n",
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"from llama_index.vector_stores.qdrant import QdrantVectorStore\n",
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"from llama_index.core import Settings\n",
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"import qdrant_client"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# allows nested access to the event loop\n",
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"nest_asyncio.apply()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# add your documents in this directory, you can drag & drop\n",
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"input_dir_path = '/teamspace/studios/this_studio/test-dir'"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"collection_name=\"chat_with_docs\"\n",
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"\n",
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"client = qdrant_client.QdrantClient(\n",
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" host=\"localhost\",\n",
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" port=6333\n",
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")\n",
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"\n",
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"def create_index(documents):\n",
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" vector_store = QdrantVectorStore(client=client, collection_name=collection_name)\n",
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" storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
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" index = VectorStoreIndex.from_documents(\n",
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" documents,\n",
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" storage_context=storage_context,\n",
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" )\n",
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" return index"
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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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"outputs": [],
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"source": []
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7b4ba9e36b4e47b982be21b95b24a181",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"config.json: 0%| | 0.00/779 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"model_id": "bf2ebc67bf4a4caf8c6292b80f869b7c",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"model.safetensors: 0%| | 0.00/1.34G [00:00<?, ?B/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"tokenizer_config.json: 0%| | 0.00/366 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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"data": {
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"model_id": "17460d4930c241c8a7af9208b82d1310",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"tokenizer.json: 0%| | 0.00/711k [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"model_id": "f73ccdc9f6be4b9e9e5d69d3de936ec1",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"special_tokens_map.json: 0%| | 0.00/125 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"\n",
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"# setup llm & embedding model\n",
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"llm=Ollama(model=\"llama3.3\", request_timeout=120.0)\n",
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"# embed_model = HuggingFaceEmbedding( model_name=\"Snowflake/snowflake-arctic-embed-m\", trust_remote_code=True)\n",
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"embed_model = HuggingFaceEmbedding( model_name=\"BAAI/bge-large-en-v1.5\", trust_remote_code=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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b9f486b6a1da4f15bb0e43469fa8c420",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Parsing nodes: 0%| | 0/17 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "363a055481fb4d808da9551727ee5307",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Generating embeddings: 0%| | 0/26 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# load data\n",
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"loader = SimpleDirectoryReader(\n",
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" input_dir = input_dir_path,\n",
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" required_exts=[\".pdf\"],\n",
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" recursive=True\n",
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" )\n",
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"docs = loader.load_data()\n",
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"\n",
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"# Creating an index over loaded data\n",
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"Settings.embed_model = embed_model\n",
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"try:\n",
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" index = create_index(docs)\n",
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" print('Using Qdrant collection')\n",
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"except:\n",
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" index = VectorStoreIndex.from_documents(docs, show_progress=True)\n",
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"\n",
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"# Create the query engine, where we use a cohere reranker on the fetched nodes\n",
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"Settings.llm = llm\n",
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"query_engine = index.as_query_engine()\n",
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"\n",
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"# ====== Customise prompt template ======\n",
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"qa_prompt_tmpl_str = (\n",
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"\"Context information is below.\\n\"\n",
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"\"---------------------\\n\"\n",
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"\"{context_str}\\n\"\n",
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"\"---------------------\\n\"\n",
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"\"Given the context information above I want you to think step by step to answer the query in a crisp manner, incase case you don't know the answer say 'I don't know!'.\\n\"\n",
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"\"Query: {query_str}\\n\"\n",
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"\"Answer: \"\n",
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")\n",
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"qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)\n",
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"\n",
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"query_engine.update_prompts(\n",
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" {\"response_synthesizer:text_qa_template\": qa_prompt_tmpl}\n",
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")\n",
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"\n",
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"# Generate the response\n",
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"response = query_engine.query(\"What exactly is DSPy?\",)"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"DSPy is a framework for programmatically solving advanced tasks with language and retrieval models through composing and declaring modules. It aims to replace brittle \"prompt engineering\" tricks with composable modules and automatic optimizers, allowing developers to define signatures that specify what a language model (LM) needs to do declaratively."
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"display(Markdown(str(response)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ❗️❗️ Make sure you clear GPU memory by clicking on Restart button above, if you want to use Streamlit from here"
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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": 10,
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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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"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
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"To disable this warning, you can either:\n",
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"\t- Avoid using `tokenizers` before the fork if possible\n",
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"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
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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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"Sat Dec 7 08:31:49 2024 \n",
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"+---------------------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 535.216.03 Driver Version: 535.216.03 CUDA Version: 12.2 |\n",
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"|-----------------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
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"| | | MIG M. |\n",
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"|=========================================+======================+======================|\n",
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"| 0 NVIDIA L4 Off | 00000000:35:00.0 Off | 0 |\n",
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"| N/A 36C P0 31W / 72W | 19895MiB / 23034MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-----------------------------------------+----------------------+----------------------+\n",
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" \n",
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"+---------------------------------------------------------------------------------------+\n",
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"| Processes: |\n",
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"| GPU GI CI PID Type Process name GPU Memory |\n",
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"| ID ID Usage |\n",
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"|=======================================================================================|\n",
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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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"# check GPU usage\n",
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"\n",
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"!nvidia-smi"
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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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"outputs": [],
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"source": []
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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",
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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": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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