prompt ergonomics #7799
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prompt ergonomics #7799
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dosubot
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hinthornw
reviewed
Jul 16, 2023
langchain/schema/messages.py
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| @@ -77,6 +77,13 @@ def lc_serializable(self) -> bool: | |||
| """Whether this class is LangChain serializable.""" | |||
| return True | |||
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| # Cannot type this since it returns something we can't import | |||
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could maybe gate on TYPE_CHECKING but maybe not
baskaryan
reviewed
Jul 16, 2023
langchain/prompts/chat.py
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| @@ -164,6 +168,18 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC): | |||
| input_variables: List[str] | |||
| messages: List[Union[BaseMessagePromptTemplate, BaseMessage]] | |||
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| def __or__(self, other: Any) -> ChatPromptTemplate: | |||
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feels weird to use or for this, would add not make more sense? like adding strings
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For piping it seems like it'd make more sense to do it between a prompt and an LLM (basically decompose an llm chain)
parsed_result = my_prompt | HuggingFace() | my_parser;
hinthornw
reviewed
Jul 17, 2023
langchain/prompts/prompt.py
Outdated
| input_variables = list( | ||
| set(self.input_variables) | set(other.input_variables) | ||
| ) | ||
| template = self.template + "\n\n" + other.template |
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It feels a bit limiting to always insert the double newline, especially if we can do concatenation with strings directly. I think I'd rather be able to do
prompt = (
PromptTemplate.from_template("Tell me a joke about {topic}")
+ "\n\nmake it funny"
+ ", and in {language}"
)
And be explicit rather than have that be magical or restrictive
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