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In the section `Get Message Completions from a Chat Model` of the quick start guide, the HumanMessage doesn't need to include `Translate this sentence from English to French.` when there is a system message. Simplify HumanMessages in these examples can further demonstrate the power of LLM.
412 lines
11 KiB
Plaintext
412 lines
11 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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"# Getting Started\n",
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"\n",
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"This notebook covers how to get started with chat models. The interface is based around messages rather than raw text."
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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 import PromptTemplate, LLMChain\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": "markdown",
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"id": "bbaec18e-3684-4eef-955f-c1cec8bf765d",
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"metadata": {},
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"source": [
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"You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`"
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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": "76a6e7b0-e927-4bfb-a414-1332a4149106",
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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={})"
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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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"chat([HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a62153d4-1211-411b-a493-3febfe446ae0",
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"metadata": {},
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"source": [
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"OpenAI's chat model supports multiple messages as input. See [here](https://platform.openai.com/docs/guides/chat/chat-vs-completions) for more information. Here is an example of sending a system and user message to the chat model:"
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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": "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={})"
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]
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},
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"execution_count": 4,
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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=\"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": "36dc8d7e-bd25-47ac-8c1b-60e3422603d3",
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"metadata": {},
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"source": [
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"You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter."
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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": "2b21fc52-74b6-4950-ab78-45d12c68fb4d",
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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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"LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})"
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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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"batch_messages = [\n",
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" [\n",
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" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
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" HumanMessage(content=\"I love programming.\")\n",
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" ],\n",
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" [\n",
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" SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
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" HumanMessage(content=\"I love artificial intelligence.\")\n",
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" ],\n",
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"]\n",
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"result = chat.generate(batch_messages)\n",
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"result"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2960f50f",
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"metadata": {},
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"source": [
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"You can recover things like token usage from this LLMResult"
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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": "a6186bee",
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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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"{'token_usage': {'prompt_tokens': 57,\n",
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" 'completion_tokens': 20,\n",
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" 'total_tokens': 77}}"
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]
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},
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"execution_count": 6,
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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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"result.llm_output"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b10b00ef-f373-4bc3-8302-2dfc28033734",
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"metadata": {},
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"source": [
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"## PromptTemplates"
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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": 7,
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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": 8,
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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": 8,
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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": "markdown",
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"id": "e28b98da",
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"metadata": {},
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"source": [
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"If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:"
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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": "d5b1ab1c",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt=PromptTemplate(\n",
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" template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
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" input_variables=[\"input_language\", \"output_language\"],\n",
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")\n",
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"system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "92af0bba",
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"metadata": {},
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"source": [
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"## LLMChain\n",
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"You can use the existing LLMChain in a very similar way to before - provide a prompt and a model."
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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": "f2cbfe3d",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = LLMChain(llm=chat, prompt=chat_prompt)"
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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": 11,
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"id": "268543b1",
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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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"\"J'adore la programmation.\""
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]
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},
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"execution_count": 11,
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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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"chain.run(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb779f3f",
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"metadata": {},
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"source": [
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"## Streaming\n",
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"\n",
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"Streaming is supported for `ChatOpenAI` through callback handling."
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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": "509181be",
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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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"\n",
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"\n",
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"Verse 1:\n",
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"Bubbles rising to the top\n",
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"A refreshing drink that never stops\n",
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"Clear and crisp, it's pure delight\n",
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"A taste that's sure to excite\n",
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"\n",
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"Chorus:\n",
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"Sparkling water, oh so fine\n",
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"A drink that's always on my mind\n",
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"With every sip, I feel alive\n",
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"Sparkling water, you're my vibe\n",
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"\n",
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"Verse 2:\n",
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"No sugar, no calories, just pure bliss\n",
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"A drink that's hard to resist\n",
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"It's the perfect way to quench my thirst\n",
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"A drink that always comes first\n",
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"\n",
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"Chorus:\n",
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"Sparkling water, oh so fine\n",
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"A drink that's always on my mind\n",
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"With every sip, I feel alive\n",
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"Sparkling water, you're my vibe\n",
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"\n",
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"Bridge:\n",
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"From the mountains to the sea\n",
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"Sparkling water, you're the key\n",
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"To a healthy life, a happy soul\n",
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"A drink that makes me feel whole\n",
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"\n",
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"Chorus:\n",
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"Sparkling water, oh so fine\n",
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"A drink that's always on my mind\n",
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"With every sip, I feel alive\n",
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"Sparkling water, you're my vibe\n",
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"\n",
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"Outro:\n",
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"Sparkling water, you're the one\n",
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"A drink that's always so much fun\n",
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"I'll never let you go, my friend\n",
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"Sparkling"
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]
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}
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],
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"source": [
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
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"resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])\n"
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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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