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144 lines
3.5 KiB
Plaintext
144 lines
3.5 KiB
Plaintext
{
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"cells": [
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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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"# Modal\n",
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"\n",
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"The [Modal Python Library](https://modal.com/docs/guide) provides convenient, on-demand access to serverless cloud compute from Python scripts on your local computer. \n",
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"The `Modal` itself does not provide any LLMs but only the infrastructure.\n",
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"\n",
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"This example goes over how to use LangChain to interact with `Modal`.\n",
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"\n",
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"[Here](https://modal.com/docs/guide/ex/potus_speech_qanda) is another example how to use LangChain to interact with `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": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install modal-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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"tags": []
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},
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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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"\u001b[?25lLaunching login page in your browser window\u001b[33m...\u001b[0m\n",
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"\u001b[2KIf this is not showing up, please copy this URL into your web browser manually:\n",
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"\u001b[2Km⠙\u001b[0m Waiting for authentication in the web browser...\n",
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"\u001b]8;id=417802;https://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\u001b\\\u001b[4;94mhttps://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\u001b[0m\u001b]8;;\u001b\\\n",
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"\n",
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"\u001b[2K\u001b[32m⠙\u001b[0m Waiting for authentication in the web browser...\n",
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"\u001b[1A\u001b[2K^C\n",
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"\n",
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"\u001b[31mAborted.\u001b[0m\n"
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]
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}
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],
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"source": [
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"# register and get a new token\n",
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"\n",
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"!modal token new"
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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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"Follow [these instructions](https://modal.com/docs/guide/secrets) to deal with secrets."
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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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"from langchain.llms import Modal\n",
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"from langchain import PromptTemplate, LLMChain"
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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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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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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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"llm = Modal(endpoint_url=\"YOUR_ENDPOINT_URL\")"
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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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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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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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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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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 (ipykernel)",
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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": "3.10.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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}
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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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