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Add LCEL to prompt doc (#11875)

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docs/docs/modules/model_io/prompts/prompt_templates/index.ipynb
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{
"cells": [
{
"cell_type": "raw",
"id": "77dd0c90-94d7-4acd-a360-e977b39d0a8f",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 0\n",
"title: Prompt templates\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "2d98412d-fc53-42c1-aed8-f1f8eb9ada58",
"metadata": {},
"source": [
"Prompt templates are pre-defined recipes for generating prompts for language models.\n",
"\n",
"A template may include instructions, few-shot examples, and specific context and\n",
"questions appropriate for a given task.\n",
"\n",
"LangChain provides tooling to create and work with prompt templates.\n",
"\n",
"LangChain strives to create model agnostic templates to make it easy to reuse\n",
"existing templates across different language models.\n",
"\n",
"Typically, language models expect the prompt to either be a string or else a list of chat messages.\n",
"\n",
"## `PromptTemplate`\n",
"\n",
"Use `PromptTemplate` to create a template for a string prompt.\n",
"\n",
"By default, `PromptTemplate` uses [Python's str.format](https://docs.python.org/3/library/stdtypes.html#str.format)\n",
"syntax for templating."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a5bc258b-87d2-486b-9785-edf5b23fd179",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Tell me a funny joke about chickens.'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.prompts import PromptTemplate\n",
"\n",
"prompt_template = PromptTemplate.from_template(\n",
" \"Tell me a {adjective} joke about {content}.\"\n",
")\n",
"prompt_template.format(adjective=\"funny\", content=\"chickens\")"
]
},
{
"cell_type": "markdown",
"id": "d54c803c-0f80-412d-9156-b8390e0265c0",
"metadata": {},
"source": [
"The template supports any number of variables, including no variables:n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "63bd7ac3-5cf6-4eb2-8205-d1a01029b56a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Tell me a joke'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.prompts import PromptTemplate\n",
"\n",
"prompt_template = PromptTemplate.from_template(\n",
"\"Tell me a joke\"\n",
")\n",
"prompt_template.format()"
]
},
{
"cell_type": "markdown",
"id": "69f7c948-9f78-431a-a466-8038e6b6f856",
"metadata": {},
"source": [
"For additional validation, specify `input_variables` explicitly. These variables\n",
"will be compared against the variables present in the template string during instantiation, **raising an exception if\n",
"there is a mismatch**. For example:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "617d7b2c-7308-4e74-9cc9-96ee0b7a13ac",
"metadata": {},
"outputs": [
{
"ename": "ValidationError",
"evalue": "1 validation error for PromptTemplate\n__root__\n Invalid prompt schema; check for mismatched or missing input parameters. 'content' (type=value_error)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[19], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprompts\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PromptTemplate\n\u001b[0;32m----> 3\u001b[0m invalid_prompt \u001b[38;5;241m=\u001b[39m \u001b[43mPromptTemplate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madjective\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemplate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTell me a \u001b[39;49m\u001b[38;5;132;43;01m{adjective}\u001b[39;49;00m\u001b[38;5;124;43m joke about \u001b[39;49m\u001b[38;5;132;43;01m{content}\u001b[39;49;00m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 6\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/langchain/libs/langchain/langchain/load/serializable.py:97\u001b[0m, in \u001b[0;36mSerializable.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lc_kwargs \u001b[38;5;241m=\u001b[39m kwargs\n",
"File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for PromptTemplate\n__root__\n Invalid prompt schema; check for mismatched or missing input parameters. 'content' (type=value_error)"
]
}
],
"source": [
"from langchain.prompts import PromptTemplate\n",
"\n",
"invalid_prompt = PromptTemplate(\n",
" input_variables=[\"adjective\"],\n",
" template=\"Tell me a {adjective} joke about {content}.\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "2715fd80-e294-49ca-9fc2-5a012949ed8a",
"metadata": {},
"source": [
"You can create custom prompt templates that format the prompt in any way you want.\n",
"For more information, see [Custom Prompt Templates](./custom_prompt_template.html).\n",
"\n",
"## `ChatPromptTemplate`\n",
"\n",
"The prompt to [chat models](../models/chat) is a list of chat messages.\n",
"\n",
"Each chat message is associated with content, and an additional parameter called `role`.\n",
"For example, in the OpenAI [Chat Completions API](https://platform.openai.com/docs/guides/chat/introduction), a chat message can be associated with an AI assistant, a human or a system role.\n",
"\n",
"Create a chat prompt template like this:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d088d53c-0e20-4fb9-9d54-b0e989b998b0",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import ChatPromptTemplate\n",
"\n",
"chat_template = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"You are a helpful AI bot. Your name is {name}.\"),\n",
" (\"human\", \"Hello, how are you doing?\"),\n",
" (\"ai\", \"I'm doing well, thanks!\"),\n",
" (\"human\", \"{user_input}\"),\n",
"])\n",
"\n",
"messages = chat_template.format_messages(\n",
" name=\"Bob\",\n",
" user_input=\"What is your name?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d1e7e3ef-ba7d-4ca5-a95c-a0488c9679e5",
"metadata": {},
"source": [
"`ChatPromptTemplate.from_messages` accepts a variety of message representations.\n",
"\n",
"For example, in addition to using the 2-tuple representation of (type, content) used\n",
"above, you could pass in an instance of `MessagePromptTemplate` or `BaseMessage`."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f6632eda-582f-4f29-882f-108587f0397c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='I absolutely love indulging in delicious treats!')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts import HumanMessagePromptTemplate\n",
"from langchain.schema.messages import SystemMessage\n",
"\n",
"chat_template = ChatPromptTemplate.from_messages(\n",
" [\n",
" SystemMessage(\n",
" content=(\n",
" \"You are a helpful assistant that re-writes the user's text to \"\n",
" \"sound more upbeat.\"\n",
" )\n",
" ),\n",
" HumanMessagePromptTemplate.from_template(\"{text}\"),\n",
" ]\n",
")\n",
"\n",
"llm = ChatOpenAI()\n",
"llm(chat_template.format_messages(text='i dont like eating tasty things.'))"
]
},
{
"cell_type": "markdown",
"id": "8c4b46da-d51b-4801-955f-ba4bf139162f",
"metadata": {},
"source": [
"This provides you with a lot of flexibility in how you construct your chat prompts."
]
},
{
"cell_type": "markdown",
"id": "3a5fe78c-572c-4e87-b02f-7d33126fb605",
"metadata": {},
"source": [
"## LCEL\n",
"\n",
"`PromptTemplate` and `ChatPromptTemplate` implement the [Runnable interface](/docs/expression_language/interface), the basic building block of the [LangChain Expression Language (LCEL)](/docs/expression_language/). This means they support `invoke`, `ainvoke`, `stream`, `astream`, `batch`, `abatch`, `astream_log` calls.\n",
"\n",
"`PromptTemplate` accepts a dictionary (of the prompt variables) and returns a `StringPromptValue`. A `ChatPromptTemplate` accepts a dictionary and returns a `ChatPromptValue`."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0f0e860b-95e0-4653-8bab-c5d58b0f7d67",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StringPromptValue(text='Tell me a joke')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt_val = prompt_template.invoke({\"adjective\": \"funny\", \"content\": \"chickens\"})\n",
"prompt_val"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "c0dac782-5144-4489-8d77-eba47f1cd1c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Tell me a joke'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt_val.to_string()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "a8e3ac32-f690-4d3d-bcb2-27b7931beab2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='Tell me a joke')]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt_val.to_messages()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4516257f-0c3b-4851-9e82-8c9e09111444",
"metadata": {},
"outputs": [],
"source": [
"chat_val = chat_template.invoke({\"text\": 'i dont like eating tasty things.'})"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "7adfe927-ba1d-425f-904c-0328e1a10c18",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content=\"You are a helpful assistant that re-writes the user's text to sound more upbeat.\"),\n",
" HumanMessage(content='i dont like eating tasty things.')]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_val.to_messages()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "37c9e2e4-a2e8-48a9-a732-01c025a21362",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"System: You are a helpful assistant that re-writes the user's text to sound more upbeat.\\nHuman: i dont like eating tasty things.\""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_val.to_string()"
]
}
],
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"language": "python",
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"nbformat_minor": 5
}

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docs/docs/modules/model_io/prompts/prompt_templates/index.mdx
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@ -1,133 +0,0 @@
---
sidebar_position: 0
---
# Prompt templates
Prompt templates are pre-defined recipes for generating prompts for language models.
A template may include instructions, few-shot examples, and specific context and
questions appropriate for a given task.
LangChain provides tooling to create and work with prompt templates.
LangChain strives to create model agnostic templates to make it easy to reuse
existing templates across different language models.
Typically, language models expect the prompt to either be a string or else a list of chat messages.
## Prompt template
Use `PromptTemplate` to create a template for a string prompt.
By default, `PromptTemplate` uses [Python's str.format](https://docs.python.org/3/library/stdtypes.html#str.format)
syntax for templating; however other templating syntax is available (e.g., `jinja2`).
```python
from langchain.prompts import PromptTemplate
prompt_template = PromptTemplate.from_template(
"Tell me a {adjective} joke about {content}."
)
prompt_template.format(adjective="funny", content="chickens")
```
<CodeOutputBlock lang="python">
```
"Tell me a funny joke about chickens."
```
</CodeOutputBlock>
The template supports any number of variables, including no variables:
```python
from langchain.prompts import PromptTemplate
prompt_template = PromptTemplate.from_template(
"Tell me a joke"
)
prompt_template.format()
```
For additional validation, specify `input_variables` explicitly. These variables
will be compared against the variables present in the template string during instantiation, raising an exception if
there is a mismatch; for example,
```python
from langchain.prompts import PromptTemplate
invalid_prompt = PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke about {content}."
)
```
You can create custom prompt templates that format the prompt in any way you want.
For more information, see [Custom Prompt Templates](./custom_prompt_template.html).
<!-- TODO(shreya): Add link to Jinja -->
## Chat prompt template
The prompt to [chat models](../models/chat) is a list of chat messages.
Each chat message is associated with content, and an additional parameter called `role`.
For example, in the OpenAI [Chat Completions API](https://platform.openai.com/docs/guides/chat/introduction), a chat message can be associated with an AI assistant, a human or a system role.
Create a chat prompt template like this:
```python
from langchain.prompts import ChatPromptTemplate
template = ChatPromptTemplate.from_messages([
("system", "You are a helpful AI bot. Your name is {name}."),
("human", "Hello, how are you doing?"),
("ai", "I'm doing well, thanks!"),
("human", "{user_input}"),
])
messages = template.format_messages(
name="Bob",
user_input="What is your name?"
)
```
`ChatPromptTemplate.from_messages` accepts a variety of message representations.
For example, in addition to using the 2-tuple representation of (type, content) used
above, you could pass in an instance of `MessagePromptTemplate` or `BaseMessage`.
```python
from langchain.prompts import ChatPromptTemplate
from langchain.prompts.chat import SystemMessage, HumanMessagePromptTemplate
template = ChatPromptTemplate.from_messages(
[
SystemMessage(
content=(
"You are a helpful assistant that re-writes the user's text to "
"sound more upbeat."
)
),
HumanMessagePromptTemplate.from_template("{text}"),
]
)
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI()
llm(template.format_messages(text='i dont like eating tasty things.'))
```
<CodeOutputBlock lang="python">
```
AIMessage(content='I absolutely adore indulging in delicious treats!', additional_kwargs={}, example=False)
```
</CodeOutputBlock>
This provides you with a lot of flexibility in how you construct your chat prompts.
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