mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
5f30cc8713
Co-authored-by: Yuichi Tateno (secon) <hotchpotch@users.noreply.github.com>
155 lines
3.9 KiB
Plaintext
155 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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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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"This notebook covers how to get started with OpenAI chat 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": 1,
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"id": "522686de",
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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.chat_models import ChatOpenAI\n",
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"from langchain.prompts.chat import (\n",
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" ChatPromptTemplate,\n",
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" SystemMessagePromptTemplate,\n",
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" AIMessagePromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.schema import (\n",
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" AIMessage,\n",
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" HumanMessage,\n",
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" SystemMessage\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": 2,
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"id": "62e0dbc3",
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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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"chat = ChatOpenAI(temperature=0)"
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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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"id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
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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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"AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 3,
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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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"messages = [\n",
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" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
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" HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
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"]\n",
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"chat(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
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"metadata": {},
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"source": [
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"You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
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"\n",
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"For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
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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": "180c5cc8",
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"metadata": {},
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"outputs": [],
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"source": [
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"template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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"system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
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"human_template=\"{text}\"\n",
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"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
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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": "fbb043e6",
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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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"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={})"
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]
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},
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"execution_count": 5,
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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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"chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
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"\n",
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"# get a chat completion from the formatted messages\n",
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"chat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages())"
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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": "c095285d",
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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 (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": "3.9.1"
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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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