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Tooling seems to work over all models.
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‎llms_wrapper/version.py‎

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import importlib.metadata
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__version__ = "0.1.34"
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__version__ = "0.2.0"
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‎notebooks/test-tooling.ipynb‎

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"answer_query(QUERIES[8], llm=\"openai/gpt-4o\", debug=False, show=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2113d314",
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"metadata": {},
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"source": [
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"## Test other LLMS"
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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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"execution_count": 50,
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"id": "e0b36066",
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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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"LLM: openai/gpt-4o\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: openai/gpt-4o-mini\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: gemini/gemini-2.0-flash-exp\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: gemini/gemini-1.5-flash\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: gemini/gemini-1.5-pro\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: anthropic/claude-3-5-sonnet-20240620\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: anthropic/claude-3-opus-20240229\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: mistral/mistral-large-latest\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: xai/grok-beta\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: groq/llama3-70b-8192\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: groq/llama-3.3-70b-versatile\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\n",
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"LLM: deepseek/deepseek-chat\n"
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]
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},
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{
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"data": {
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"text/markdown": [
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"Query: What is the name of the department John Doe works in?\n",
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"\n",
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"Answer: John Doe works in the HR department.\n",
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"\n",
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"Cost: 0.005665"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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"for llmalias in llms.list_aliases():\n",
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" print(f\"LLM: {llmalias}\")\n",
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" answer_query(QUERIES[0], llm=\"openai/gpt-4o\", debug=False, show=True)\n",
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" print(\"-\" * 80)\n",
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" print()"
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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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"id": "aae0d86a",
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"metadata": {},
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"outputs": [],
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"source": []
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}

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