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Harrison Chase
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# Prompts
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Prompts and all the tooling around them are integral to working with language models, and therefor
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really important to get right, from both and interface and naming perspective. This is a "design doc"
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of sorts explaining how we think about prompts and the related concepts, and why the interfaces
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for working with are the way they are in LangChain.
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For a more code-based walkthrough of all these concept, checkout our example [here](/examples/prompts/prompt_management)
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## Prompt
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### Concept
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A prompt is the final string that gets fed into the language model.
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### LangChain Implementation
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In LangChain a prompt is represented as just a string.
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## Input Variables
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### Concept
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Input variables are parts of a prompt that are not known until runtime, eg could be user provided.
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### LangChain Implementation
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In LangChain input variables are just represented as a dictionary of key-value pairs, with the key
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being the variable name and the value being the variable value.
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## Examples
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### Concept
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Examples are basically datapoints that can be used to teach the model what to do. These can be included
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in prompts to better instruct the model on what to do.
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### LangChain Implementation
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In LangChain examples are represented as a dictionary of key-value pairs, with the key being the feature
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(or label) name, and the value being the feature (or label) value.
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## Example Selector
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### Concept
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If you have a large number of examples, you may need to select which ones to include in the prompt. The
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Example Selector is the class responsible for doing so.
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### LangChain Implementation
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#### BaseExampleSelector
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In LangChain there is a BaseExampleSelector that exposes the following interface
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```python
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class BaseExampleSelector:
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def select_examples(self, input_variables: dict):
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```
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Notice that it does not take in examples at runtime when it's selecting them - those are assumed to have been provided ahead of time.
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#### LengthExampleSelector
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The LengthExampleSelector selects examples based on the length of the input variables.
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This is useful when you are worried about constructing a prompt that will go over the length
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of the context window. For longer inputs, it will select fewer examples to include, while for
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shorter inputs it will select more.
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#### SemanticSimilarityExampleSelector
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The SemanticSimilarityExampleSelector selects examples based on which examples are most similar
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to the inputs. It does this by finding the examples with the embeddings that have the greatest
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cosine similarity with the inputs.
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## Prompt Template
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### Concept
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The prompts that get fed into the language model are nearly always not hardcoded, but rather a combination
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of parts, including Examples and Input Variables. A prompt template is responsible
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for taking those parts and constructing a prompt.
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### LangChain Implementation
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#### BasePromptTemplate
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In LangChain there is a BasePromptTemplate that exposes the following interface
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```python
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class BasePromptTemplate:
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@property
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def input_variables(self) -> List[str]:
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def format(self, **kwargs) -> str:
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```
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The input variables property is used to provide introspection of the PromptTemplate and know
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what inputs it expects. The format method takes in input variables and returns the prompt.
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#### PromptTemplate
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The PromptTemplate implementation is the most simple form of a prompt template. It consists of three parts:
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- input variables: which variables this prompt template expects
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- template: the template into which these variables will be formatted
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- template format: the format of the template (eg mustache, python f-strings, etc)
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For example, if I was making an application that took a user inputted concept and asked a language model
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to make a joke about that concept, I might use this specification for the PromptTemplate
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- input variables = `["thing"]`
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- template = `"Tell me a joke about {thing}"`
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- template format = `"f-string"`
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#### FewShotPromptTemplate
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A FewShotPromptTemplate is a Prompt Template that includes some examples. It consists of:
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- examples OR example selector: a list of examples to use, or an Example Selector to select which examples to use
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- example prompt template: a Prompt Template responsible for taking an individual example (a dictionary) and turning it into a string to be used in the prompt.
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- prefix: the template put in the prompt before listing any examples
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- suffix: the template put in the prompt after listing any examples
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- example separator: a string separator which is used to join the prefix, the examples, and the suffix together
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For example, if I wanted to turn the above example into a few shot prompt, this is what it would
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look like:
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First I would collect some examples, like
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```python
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examples = [
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{"concept": "chicken", "joke": "Why did the chicken cross the road?"},
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...
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]
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```
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I would then make sure to define a prompt template for how each example should be formatted
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when inserted into the prompt:
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```python
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prompt_template = PromptTemplate(
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input_variables=["concept", "joke"],
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template="Tell me a joke about {concept}\n{joke}"
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)
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```
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Then, I would define the components as:
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- examples: The above examples
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- example_prompt: The above example prompt
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- prefix = `"You are a comedian telling jokes on demand."`
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- suffix = `"Tell me a joke about {concept}"`
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- input variables = `["concept"]`
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- template format = `"f-string"`
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