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Harrison/prompt constructor methods (#4616)
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Harrison Chase
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@ -36,6 +36,15 @@ This is where output parsers come in.
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Output Parsers are responsible for (1) instructing the model how output should be formatted,
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(2) parsing output into the desired formatting (including retrying if necessary).
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Getting Started
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---------------
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.. toctree::
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:maxdepth: 1
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./prompts/getting_started.ipynb
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Go Deeper
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---------
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218
docs/modules/prompts/getting_started.ipynb
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218
docs/modules/prompts/getting_started.ipynb
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@ -0,0 +1,218 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3651e424",
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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 section contains everything related to prompts. A prompt is the value passed into the Language Model. This value can either be a string (for LLMs) or a list of messages (for Chat Models).\n",
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"\n",
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"The data types of these prompts are rather simple, but their construction is anything but. Value props of LangChain here include:\n",
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"\n",
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"- A standard interface for string prompts and message prompts\n",
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"- A standard (to get started) interface for string prompt templates and message prompt templates\n",
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"- Example Selectors: methods for inserting examples into the prompt for the language model to follow\n",
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"- OutputParsers: methods for inserting instructions into the prompt as the format in which the language model should output information, as well as methods for then parsing that string output into a format.\n",
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"\n",
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"We have in depth documentation for specific types of string prompts, specific types of chat prompts, example selectors, and output parsers.\n",
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"\n",
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"Here, we cover a quick-start for a standard interface for getting started with simple prompts."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ff34414d",
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"metadata": {},
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"source": [
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"## PromptTemplates\n",
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"\n",
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"PromptTemplates are responsible for constructing a prompt value. These PromptTemplates can do things like formatting, example selection, and more. At a high level, these are basically objects that expose a `format_prompt` method for constructing a prompt. Under the hood, ANYTHING can happen."
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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": 13,
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"id": "7ce42639",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate, ChatPromptTemplate"
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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": 14,
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"id": "5a178697",
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"metadata": {},
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"outputs": [],
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"source": [
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"string_prompt = PromptTemplate.from_template(\"tell me a joke about {subject}\")"
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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": 15,
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"id": "f4ef6d6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"chat_prompt = ChatPromptTemplate.from_template(\"tell me a joke about {subject}\")"
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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": 16,
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"id": "5f16c8f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"string_prompt_value = string_prompt.format_prompt(subject=\"soccer\")"
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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": 17,
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"id": "863755ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"chat_prompt_value = chat_prompt.format_prompt(subject=\"soccer\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b3d8511",
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"metadata": {},
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"source": [
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"## `to_string`\n",
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"\n",
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"This is what is called when passing to an LLM (which expects 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": 18,
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"id": "1964a8a0",
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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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"'tell me a joke about soccer'"
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]
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},
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"execution_count": 18,
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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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"string_prompt_value.to_string()"
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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": 19,
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"id": "bf6c94e9",
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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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"'Human: tell me a joke about soccer'"
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]
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},
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"execution_count": 19,
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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_value.to_string()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c0825af8",
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"metadata": {},
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"source": [
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"## `to_messages`\n",
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"\n",
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"This is what is called when passing to ChatModel (which expects a list of 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": 20,
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"id": "e4da46f3",
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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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"[HumanMessage(content='tell me a joke about soccer', additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 20,
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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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"string_prompt_value.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": 21,
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"id": "eae84b88",
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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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"[HumanMessage(content='tell me a joke about soccer', additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 21,
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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_value.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": "a34fa440",
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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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@ -143,13 +143,19 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
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input_variables: List[str]
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messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
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@classmethod
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def from_template(cls, template: str, **kwargs: Any) -> ChatPromptTemplate:
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prompt_template = PromptTemplate.from_template(template, **kwargs)
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message = HumanMessagePromptTemplate(prompt=prompt_template)
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return cls.from_messages([message])
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@classmethod
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def from_role_strings(
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cls, string_messages: List[Tuple[str, str]]
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) -> ChatPromptTemplate:
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messages = [
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ChatMessagePromptTemplate(
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content=PromptTemplate.from_template(template), role=role
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prompt=PromptTemplate.from_template(template), role=role
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)
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for role, template in string_messages
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]
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@ -160,7 +166,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
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cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
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) -> ChatPromptTemplate:
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messages = [
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role(content=PromptTemplate.from_template(template))
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role(prompt=PromptTemplate.from_template(template))
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for role, template in string_messages
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]
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return cls.from_messages(messages)
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