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# Clarification of the reference to the "get_text_legth" function in getting_started.md Reference to the function "get_text_legth" in the documentation did not make sense. Comment added for clarification. @hwchase17
290 lines
11 KiB
Markdown
290 lines
11 KiB
Markdown
# Getting Started
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In this tutorial, we will learn about:
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- what a prompt template is, and why it is needed,
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- how to create a prompt template,
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- how to pass few shot examples to a prompt template,
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- how to select examples for a prompt template.
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## What is a prompt template?
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A prompt template refers to a reproducible way to generate a prompt. It contains a text string ("the template"), that can take in a set of parameters from the end user and generate a prompt.
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The prompt template may contain:
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- instructions to the language model,
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- a set of few shot examples to help the language model generate a better response,
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- a question to the language model.
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The following code snippet contains an example of a prompt template:
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```python
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from langchain import PromptTemplate
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template = """
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I want you to act as a naming consultant for new companies.
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What is a good name for a company that makes {product}?
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"""
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prompt = PromptTemplate(
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input_variables=["product"],
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template=template,
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)
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prompt.format(product="colorful socks")
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# -> I want you to act as a naming consultant for new companies.
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# -> What is a good name for a company that makes colorful socks?
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```
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## Create a prompt template
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You can create simple hardcoded prompts using the `PromptTemplate` class. Prompt templates can take any number of input variables, and can be formatted to generate a prompt.
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```python
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from langchain import PromptTemplate
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# An example prompt with no input variables
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no_input_prompt = PromptTemplate(input_variables=[], template="Tell me a joke.")
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no_input_prompt.format()
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# -> "Tell me a joke."
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# An example prompt with one input variable
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one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
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one_input_prompt.format(adjective="funny")
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# -> "Tell me a funny joke."
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# An example prompt with multiple input variables
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multiple_input_prompt = PromptTemplate(
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input_variables=["adjective", "content"],
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template="Tell me a {adjective} joke about {content}."
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)
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multiple_input_prompt.format(adjective="funny", content="chickens")
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# -> "Tell me a funny joke about chickens."
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```
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If you do not wish to specify `input_variables` manually, you can also create a `PromptTemplate` using `from_template` class method. `langchain` will automatically infer the `input_variables` based on the `template` passed.
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```python
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template = "Tell me a {adjective} joke about {content}."
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prompt_template = PromptTemplate.from_template(template)
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prompt_template.input_variables
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# -> ['adjective', 'content']
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prompt_template.format(adjective="funny", content="chickens")
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# -> Tell me a funny joke about chickens.
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```
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You can create custom prompt templates that format the prompt in any way you want. For more information, see [Custom Prompt Templates](examples/custom_prompt_template.ipynb).
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<!-- TODO(shreya): Add link to Jinja -->
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## Template formats
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By default, `PromptTemplate` will treat the provided template as a Python f-string. You can specify other template format through `template_format` argument:
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```python
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# Make sure jinja2 is installed before running this
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jinja2_template = "Tell me a {{ adjective }} joke about {{ content }}"
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prompt_template = PromptTemplate.from_template(template=jinja2_template, template_format="jinja2")
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prompt_template.format(adjective="funny", content="chickens")
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# -> Tell me a funny joke about chickens.
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```
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Currently, `PromptTemplate` only supports `jinja2` and `f-string` templating format. If there is any other templating format that you would like to use, feel free to open an issue in the [Github](https://github.com/hwchase17/langchain/issues) page.
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## Validate template
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By default, `PromptTemplate` will validate the `template` string by checking whether the `input_variables` match the variables defined in `template`. You can disable this behavior by setting `validate_template` to `False`
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```python
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template = "I am learning langchain because {reason}."
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prompt_template = PromptTemplate(template=template,
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input_variables=["reason", "foo"]) # ValueError due to extra variables
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prompt_template = PromptTemplate(template=template,
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input_variables=["reason", "foo"],
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validate_template=False) # No error
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```
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## Serialize prompt template
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You can save your `PromptTemplate` into a file in your local filesystem. `langchain` will automatically infer the file format through the file extension name. Currently, `langchain` supports saving template to YAML and JSON file.
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```python
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prompt_template.save("awesome_prompt.json") # Save to JSON file
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```
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```python
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from langchain.prompts import load_prompt
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loaded_prompt = load_prompt("awesome_prompt.json")
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assert prompt_template == loaded_prompt
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```
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`langchain` also supports loading prompt template from LangChainHub, which contains a collection of useful prompts you can use in your project. You can read more about LangChainHub and the prompts available with it [here](https://github.com/hwchase17/langchain-hub).
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```python
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from langchain.prompts import load_prompt
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prompt = load_prompt("lc://prompts/conversation/prompt.json")
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prompt.format(history="", input="What is 1 + 1?")
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```
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You can learn more about serializing prompt template in [How to serialize prompts](examples/prompt_serialization.ipynb).
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## Pass few shot examples to a prompt template
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Few shot examples are a set of examples that can be used to help the language model generate a better response.
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To generate a prompt with few shot examples, you can use the `FewShotPromptTemplate`. This class takes in a `PromptTemplate` and a list of few shot examples. It then formats the prompt template with the few shot examples.
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In this example, we'll create a prompt to generate word antonyms.
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```python
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from langchain import PromptTemplate, FewShotPromptTemplate
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# First, create the list of few shot examples.
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examples = [
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{"word": "happy", "antonym": "sad"},
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{"word": "tall", "antonym": "short"},
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]
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# Next, we specify the template to format the examples we have provided.
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# We use the `PromptTemplate` class for this.
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example_formatter_template = """Word: {word}
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Antonym: {antonym}
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"""
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example_prompt = PromptTemplate(
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input_variables=["word", "antonym"],
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template=example_formatter_template,
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)
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# Finally, we create the `FewShotPromptTemplate` object.
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few_shot_prompt = FewShotPromptTemplate(
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# These are the examples we want to insert into the prompt.
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examples=examples,
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# This is how we want to format the examples when we insert them into the prompt.
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example_prompt=example_prompt,
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# The prefix is some text that goes before the examples in the prompt.
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# Usually, this consists of intructions.
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prefix="Give the antonym of every input\n",
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# The suffix is some text that goes after the examples in the prompt.
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# Usually, this is where the user input will go
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suffix="Word: {input}\nAntonym: ",
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# The input variables are the variables that the overall prompt expects.
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input_variables=["input"],
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# The example_separator is the string we will use to join the prefix, examples, and suffix together with.
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example_separator="\n",
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)
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# We can now generate a prompt using the `format` method.
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print(few_shot_prompt.format(input="big"))
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# -> Give the antonym of every input
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# ->
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# -> Word: happy
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# -> Antonym: sad
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# ->
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# -> Word: tall
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# -> Antonym: short
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# ->
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# -> Word: big
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# -> Antonym:
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```
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## Select examples for a prompt template
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If you have a large number of examples, you can use the `ExampleSelector` to select a subset of examples that will be most informative for the Language Model. This will help you generate a prompt that is more likely to generate a good response.
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Below, we'll use the `LengthBasedExampleSelector`, which selects examples based on the length of the input. This is useful when you are worried about constructing a prompt that will go over the length of the context window. For longer inputs, it will select fewer examples to include, while for shorter inputs it will select more.
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We'll continue with the example from the previous section, but this time we'll use the `LengthBasedExampleSelector` to select the examples.
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```python
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from langchain.prompts.example_selector import LengthBasedExampleSelector
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# These are a lot of examples of a pretend task of creating antonyms.
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examples = [
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{"word": "happy", "antonym": "sad"},
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{"word": "tall", "antonym": "short"},
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{"word": "energetic", "antonym": "lethargic"},
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{"word": "sunny", "antonym": "gloomy"},
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{"word": "windy", "antonym": "calm"},
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]
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# We'll use the `LengthBasedExampleSelector` to select the examples.
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example_selector = LengthBasedExampleSelector(
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# These are the examples is has available to choose from.
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examples=examples,
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# This is the PromptTemplate being used to format the examples.
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example_prompt=example_prompt,
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# This is the maximum length that the formatted examples should be.
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# Length is measured by the get_text_length function below.
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max_length=25
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# This is the function used to get the length of a string, which is used
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# to determine which examples to include. It is commented out because
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# it is provided as a default value if none is specified.
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# get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
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)
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# We can now use the `example_selector` to create a `FewShotPromptTemplate`.
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dynamic_prompt = FewShotPromptTemplate(
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# We provide an ExampleSelector instead of examples.
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example_selector=example_selector,
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example_prompt=example_prompt,
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prefix="Give the antonym of every input",
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suffix="Word: {input}\nAntonym:",
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input_variables=["input"],
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example_separator="\n\n",
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)
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# We can now generate a prompt using the `format` method.
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print(dynamic_prompt.format(input="big"))
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# -> Give the antonym of every input
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# ->
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# -> Word: happy
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# -> Antonym: sad
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# ->
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# -> Word: tall
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# -> Antonym: short
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# ->
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# -> Word: energetic
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# -> Antonym: lethargic
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# ->
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# -> Word: sunny
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# -> Antonym: gloomy
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# ->
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# -> Word: windy
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# -> Antonym: calm
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# ->
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# -> Word: big
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# -> Antonym:
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```
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In contrast, if we provide a very long input, the `LengthBasedExampleSelector` will select fewer examples to include in the prompt.
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```python
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long_string = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"
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print(dynamic_prompt.format(input=long_string))
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# -> Give the antonym of every input
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# -> Word: happy
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# -> Antonym: sad
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# ->
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# -> Word: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else
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# -> Antonym:
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```
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<!-- TODO(shreya): Add correct link here. -->
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LangChain comes with a few example selectors that you can use. For more details on how to use them, see [Example Selectors](../example_selectors.rst).
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You can create custom example selectors that select examples based on any criteria you want. For more details on how to do this, see [Creating a custom example selector](../example_selectors/examples/custom_example_selector.md).
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