forked from Archives/langchain
5420a0e404
- Updated `langchain/docs/modules/models/llms/integrations/` notebooks: added links to the original sites, the install information, etc. - Added the `nlpcloud` notebook. - Removed "Example" from Titles of some notebooks, so all notebook titles are consistent.
159 lines
3.3 KiB
Plaintext
159 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9597802c",
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"metadata": {},
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"source": [
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"# OpenAI\n",
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"\n",
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"[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n",
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"\n",
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"This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)"
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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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"id": "5d71df86-8a17-4283-83d7-4e46e7c06c44",
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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": "stdin",
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"output_type": "stream",
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"text": [
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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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"# get a token: https://platform.openai.com/account/api-keys\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"OPENAI_API_KEY = getpass()"
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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": 5,
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"id": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
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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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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
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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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"id": "6fb585dd",
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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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"from langchain.llms import OpenAI\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": 7,
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"id": "035dea0f",
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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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"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": 8,
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"id": "3f3458d9",
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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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"llm = OpenAI()"
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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": 9,
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"id": "a641dbd9",
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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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"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": 10,
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"id": "9f844993",
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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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"data": {
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"text/plain": [
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"' Justin Bieber was born in 1994, so we are looking for the Super Bowl winner from that year. The Super Bowl in 1994 was Super Bowl XXVIII, and the winner was the Dallas Cowboys.'"
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]
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},
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"execution_count": 10,
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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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"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": 5
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}
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