FewShotChatMessagePromptTemplate Class — langchain Architecture

Source Code

libs/core/langchain_core/prompts/few_shot.py lines 255–476

class FewShotChatMessagePromptTemplate(
    BaseChatPromptTemplate, _FewShotPromptTemplateMixin
):
    """Chat prompt template that supports few-shot examples.

    The high level structure of produced by this prompt template is a list of messages
    consisting of prefix message(s), example message(s), and suffix message(s).

    This structure enables creating a conversation with intermediate examples like:

    ```txt
    System: You are a helpful AI Assistant

    Human: What is 2+2?

    AI: 4

    Human: What is 2+3?

    AI: 5

    Human: What is 4+4?
    ```

    This prompt template can be used to generate a fixed list of examples or else to
    dynamically select examples based on the input.

    Examples:
        Prompt template with a fixed list of examples (matching the sample
        conversation above):

        ```python
        from langchain_core.prompts import (
            FewShotChatMessagePromptTemplate,
            ChatPromptTemplate,
        )

        examples = [
            {"input": "2+2", "output": "4"},
            {"input": "2+3", "output": "5"},
        ]

        example_prompt = ChatPromptTemplate.from_messages(
            [
                ("human", "What is {input}?"),
                ("ai", "{output}"),
            ]
        )

        few_shot_prompt = FewShotChatMessagePromptTemplate(
            examples=examples,
            # This is a prompt template used to format each individual example.
            example_prompt=example_prompt,
        )

        final_prompt = ChatPromptTemplate.from_messages(
            [
                ("system", "You are a helpful AI Assistant"),
                few_shot_prompt,
                ("human", "{input}"),
            ]
        )
        final_prompt.format(input="What is 4+4?")
        ```

        Prompt template with dynamically selected examples:

        ```python
        from langchain_core.prompts import SemanticSimilarityExampleSelector
        from langchain_core.embeddings import OpenAIEmbeddings
        from langchain_core.vectorstores import Chroma

        examples = [
            {"input": "2+2", "output": "4"},
            {"input": "2+3", "output": "5"},
            {"input": "2+4", "output": "6"},
            # ...
        ]

        to_vectorize = [" ".join(example.values()) for example in examples]
        embeddings = OpenAIEmbeddings()