How to use few shot examples in chat models
This guide assumes familiarity with the following concepts:
This guide covers how to prompt a chat model with example inputs and outputs. Providing the model with a few such examples is calledfew-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance.
There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like theFewShotChatMessagePromptTemplate as a flexible starting point, and you can modify or replace them as you see fit.
The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.
Note: The following code examples are for chat models only, sinceFewShotChatMessagePromptTemplates are designed to output formattedchat messages rather than pure strings. For similar few-shot prompt examples for pure string templates compatible with completion models (LLMs), see thefew-shot prompt templates guide.
Fixed Examples
The most basic (and common) few-shot prompting technique is to use fixed prompt examples. This way you can select a chain, evaluate it, and avoid worrying about additional moving parts in production.
The basic components of the template are:
examples: A list of dictionary examples to include in the final prompt.example_prompt: converts each example into 1 or more messages through itsformat_messagesmethod. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.
Below is a simple demonstration. First, define the examples you'd like to include. Let's give the LLM an unfamiliar mathematical operator, denoted by the "🦜" emoji:
%pip install-qU langchain langchain-openai langchain-chroma
import os
from getpassimport getpass
if"OPENAI_API_KEY"notin os.environ:
os.environ["OPENAI_API_KEY"]= getpass()
If we try to ask the model what the result of this expression is, it will fail:
from langchain_openaiimport ChatOpenAI
model= ChatOpenAI(model="gpt-4o-mini", temperature=0.0)
model.invoke("What is 2 🦜 9?")
AIMessage(content='The expression "2 🦜 9" is not a standard mathematical operation or equation. It appears to be a combination of the number 2 and the parrot emoji 🦜 followed by the number 9. It does not have a specific mathematical meaning.', response_metadata={'token_usage': {'completion_tokens': 54, 'prompt_tokens': 17, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aad12dda-5c47-4a1e-9949-6fe94e03242a-0', usage_metadata={'input_tokens': 17, 'output_tokens': 54, 'total_tokens': 71})
Now let's see what happens if we give the LLM some examples to work with. We'll define some below:
from langchain_core.promptsimport ChatPromptTemplate, FewShotChatMessagePromptTemplate
examples=[
{"input":"2 🦜 2","output":"4"},
{"input":"2 🦜 3","output":"5"},
]
Next, assemble them into the few-shot prompt template.
# This is a prompt template used to format each individual example.
example_prompt= ChatPromptTemplate.from_messages(
[
("human","{input}"),
("ai","{output}"),
]
)
few_shot_prompt= FewShotChatMessagePromptTemplate(
example_prompt=example_prompt,
examples=examples,
)
print(few_shot_prompt.invoke({}).to_messages())
[HumanMessage(content='2 🦜 2'), AIMessage(content='4'), HumanMessage(content='2 🦜 3'), AIMessage(content='5')]
Finally, we assemble the final prompt as shown below, passingfew_shot_prompt directly into thefrom_messages factory method, and use it with a model:
final_prompt= ChatPromptTemplate.from_messages(
[
("system","You are a wondrous wizard of math."),
few_shot_prompt,
("human","{input}"),
]
)
And now let's ask the model the initial question and see how it does:
from langchain_openaiimport ChatOpenAI
chain= final_prompt| model
chain.invoke({"input":"What is 2 🦜 9?"})
AIMessage(content='11', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5ec4e051-262f-408e-ad00-3f2ebeb561c3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})
And we can see that the model has now inferred that the parrot emoji means addition from the given few-shot examples!
Dynamic few-shot prompting
Sometimes you may want to select only a few examples from your overall set to show based on the input. For this, you can replace theexamples passed intoFewShotChatMessagePromptTemplate with anexample_selector. The other components remain the same as above! Our dynamic few-shot prompt template would look like:
example_selector: responsible for selecting few-shot examples (and the order in which they are returned) for a given input. These implement theBaseExampleSelector interface. A common example is the vectorstore-backedSemanticSimilarityExampleSelectorexample_prompt: convert each example into 1 or more messages through itsformat_messagesmethod. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.
These once again can be composed with other messages and chat templates to assemble your final prompt.
Let's walk through an example with theSemanticSimilarityExampleSelector. Since this implementation uses a vectorstore to select examples based on semantic similarity, we will want to first populate the store. Since the basic idea here is that we want to search for and return examples most similar to the text input, we embed thevalues of our prompt examples rather than considering the keys:
from langchain_chromaimport Chroma
from langchain_core.example_selectorsimport SemanticSimilarityExampleSelector
from langchain_openaiimport OpenAIEmbeddings
examples=[
{"input":"2 🦜 2","output":"4"},
{"input":"2 🦜 3","output":"5"},
{"input":"2 🦜 4","output":"6"},
{"input":"What did the cow say to the moon?","output":"nothing at all"},
{
"input":"Write me a poem about the moon",
"output":"One for the moon, and one for me, who are we to talk about the moon?",
},
]
to_vectorize=[" ".join(example.values())for examplein examples]
embeddings= OpenAIEmbeddings()
vectorstore= Chroma.from_texts(to_vectorize, embeddings, metadatas=examples)
Create theexample_selector
With a vectorstore created, we can create theexample_selector. Here we will call it in isolation, and setk on it to only fetch the two example closest to the input.
example_selector= SemanticSimilarityExampleSelector(
vectorstore=vectorstore,
k=2,
)
# The prompt template will load examples by passing the input do the `select_examples` method
example_selector.select_examples({"input":"horse"})
[{'input': 'What did the cow say to the moon?', 'output': 'nothing at all'},
{'input': '2 🦜 4', 'output': '6'}]
Create prompt template
We now assemble the prompt template, using theexample_selector created above.
from langchain_core.promptsimport ChatPromptTemplate, FewShotChatMessagePromptTemplate
# Define the few-shot prompt.
few_shot_prompt= FewShotChatMessagePromptTemplate(
# The input variables select the values to pass to the example_selector
input_variables=["input"],
example_selector=example_selector,
# Define how each example will be formatted.
# In this case, each example will become 2 messages:
# 1 human, and 1 AI
example_prompt=ChatPromptTemplate.from_messages(
[("human","{input}"),("ai","{output}")]
),
)
print(few_shot_prompt.invoke(input="What's 3 🦜 3?").to_messages())
[HumanMessage(content='2 🦜 3'), AIMessage(content='5'), HumanMessage(content='2 🦜 4'), AIMessage(content='6')]
And we can pass this few-shot chat message prompt template into another chat prompt template:
final_prompt= ChatPromptTemplate.from_messages(
[
("system","You are a wondrous wizard of math."),
few_shot_prompt,
("human","{input}"),
]
)
print(few_shot_prompt.invoke(input="What's 3 🦜 3?"))
messages=[HumanMessage(content='2 🦜 3'), AIMessage(content='5'), HumanMessage(content='2 🦜 4'), AIMessage(content='6')]
Use with an chat model
Finally, you can connect your model to the few-shot prompt.
chain= final_prompt| ChatOpenAI(model="gpt-4o-mini", temperature=0.0)
chain.invoke({"input":"What's 3 🦜 3?"})
AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d1863e5e-17cd-4e9d-bf7a-b9f118747a65-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})
Next steps
You've now learned how to add few-shot examples to your chat prompts.
Next, check out the other how-to guides on prompt templates in this section, the related how-to guide onfew shotting with text completion models, or the otherexample selector how-to guides.