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test.ipynb
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"cells": [
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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 rich\n",
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"import weave\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"from medrag_multi_modal.retrieval import BM25sRetriever"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Logged in as Weights & Biases user: geekyrakshit.\n",
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"View Weave data at https://wandb.ai/ml-colabs/medrag-multi-modal/weave\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<weave.trace.weave_client.WeaveClient at 0x31bb4b200>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"load_dotenv()\n",
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"weave.init(project_name=\"ml-colabs/medrag-multi-modal\")"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 7 of 7 files downloaded. \n"
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]
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}
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],
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"source": [
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"retriever = BM25sRetriever.from_wandb_artifact(\n",
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" index_artifact_address=\"ml-colabs/medrag-multi-modal/grays-anatomy-bm25s:v2\"\n",
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")"
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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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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6247f921c889469283505348967807da",
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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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"Split strings: 0%| | 0/1 [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": "0ccb25cf58c84023846d68561962adc5",
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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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"Stem Tokens: 0%| | 0/1 [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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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d2eddb186fac447d8e7dc8f185ce7c86",
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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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"BM25S Retrieve: 0%| | 0/1 [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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"results = retriever.retrieve(query=\"What are Ribosomes?\")"
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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": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"><</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">class</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'dict'</span><span style=\"font-weight: bold\">></span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'dict'\u001b[0m\u001b[1m>\u001b[0m\n"
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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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"rich.print(list(list(results['results'])[0])[0])"
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]
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2"
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"execution_count": 21,
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"output_type": "execute_result"
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}
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],
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"source": [
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"len()"
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"execution_count": 19,
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{
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"data": {
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"text/plain": [
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"[0.4504720866680145, 0.3982057571411133]"
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"results[\"scores\"].flatten().tolist()"
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{
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"cell_type": "code",
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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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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"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": "3.12.7"
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"nbformat": 4,
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"nbformat_minor": 2
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