How to select examples by similarity

This object selects examples based on similarity to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.

from langchain_chroma import Chroma
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate
from langchain_openai import OpenAIEmbeddings

example_prompt = PromptTemplate(
    input_variables=["input", "output"],
    template="Input: {input}\nOutput: {output}",
)

# Examples of a pretend task of creating antonyms.
examples = [
    {"input": "happy", "output": "sad"},
    {"input": "tall", "output": "short"},
    {"input": "energetic", "output": "lethargic"},
    {"input": "sunny", "output": "gloomy"},
    {"input": "windy", "output": "calm"},
]

API Reference:SemanticSimilarityExampleSelector | FewShotPromptTemplate | PromptTemplate | OpenAIEmbeddings

example_selector = SemanticSimilarityExampleSelector.from_examples(
    # The list of examples available to select from.
    examples,
    # The embedding class used to produce embeddings which are used to measure semantic similarity.
    OpenAIEmbeddings(),
    # The VectorStore class that is used to store the embeddings and do a similarity search over.
    Chroma,
    # The number of examples to produce.
    k=1,
)
similar_prompt = FewShotPromptTemplate(
    # We provide an ExampleSelector instead of examples.
    example_selector=example_selector,
    example_prompt=example_prompt,
    prefix="Give the antonym of every input",
    suffix="Input: {adjective}\nOutput:",
    input_variables=["adjective"],
)

# Input is a feeling, so should select the happy/sad example
print(similar_prompt.format(adjective="worried"))

Give the antonym of every input

Input: happy
Output: sad

Input: worried
Output:

# Input is a measurement, so should select the tall/short example
print(similar_prompt.format(adjective="large"))

Give the antonym of every input

Input: tall
Output: short

Input: large
Output:

# You can add new examples to the SemanticSimilarityExampleSelector as well
similar_prompt.example_selector.add_example(
    {"input": "enthusiastic", "output": "apathetic"}
)
print(similar_prompt.format(adjective="passionate"))

Give the antonym of every input

Input: enthusiastic
Output: apathetic

Input: passionate
Output: