Implementing Few-Shot Learning with LangChain

Understanding Few-Shot Learning

Why Use LangChain?

Setting Up LangChain

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pip install langchain langchain-openai

Creating Few-Shot Prompt Templates

Step 1: Define Examples

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examples = [
    {"question": "Who lived longer, Muhammad Ali or Alan Turing?", "answer": "Muhammad Ali lived longer."},
    {"question": "When was Craigslist founded?", "answer": "Craigslist was founded in 1995."},
    {"question": "Who was George Washington's maternal grandfather?", "answer": "Joseph Ball was George Washington's maternal grandfather."},
    {"question": "Are the directors of Jaws and Casino Royale from the same country?", "answer": "No, they are from different countries."
]

Step 2: Create a Prompt Template

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from langchain.core.prompts import PromptTemplate

example_prompt = PromptTemplate(
    input_variables=["question", "answer"],
    template="Question: {question}\nAnswer: {answer}"
)

Step 3: Build FewShotPromptTemplate

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from langchain.core.prompts import FewShotPromptTemplate

prompt = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    suffix="Question: {input}",
    input_variables=["input"],
)

Step 4: Using the Template

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output = prompt.format(input="Who was George Washington's father?")
print(output)

Implementing Example Selector

Setting Up an Example Selector

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from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_openai import OpenAIEmbeddings

# Create an embedding model
embedding_model = OpenAIEmbeddings()

# Now use Chroma to create a vector store that can retrieve embeddings
from langchain.vectorstores import Chroma
vector_store = Chroma.from_documents(examples, embedding_model)

# Setup example selector
example_selector = SemanticSimilarityExampleSelector(
    vectorstore=vector_store,
    k=2  # Number of examples to select
)

Integrate Example Selector into FewShotPromptTemplate

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from langchain_core.prompts import FewShotPromptTemplate

prompt_with_selector = FewShotPromptTemplate(
    example_selector=example_selector,
    example_prompt=example_prompt,
    suffix="Question: {input}",
    input_variables=["input"],
)

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dynamic_output = prompt_with_selector.format(input="Who was President after Washington?")
print(dynamic_output)

Benefits of Few-Shot Learning with LangChain

• Efficiency: You save a substantial amount of data preparation time since you only require a few examples, unlike traditional models that need extensive datasets.
• Cost Effectiveness: Reduces the need for extensive data collection, thus minimizing operational costs related to data gathering and storage.
• Faster Execution: With fewer data, the model can quickly adapt to new tasks, providing rapid feedback loops for projects.
• Improved Performance: Leveraging few-shot learning improves model performance by guiding the LLM through specific examples, enhancing response quality.
• Flexibility: LangChain provides a modular setup that allows you to easily change and tweak how few-shot learning is implemented, dynamically adjusting to different demands.

Real-World Applications

1. Chatbots: Businesses deploy few-shot learning to enhance chatbot responses across customer service, utilizing fewer examples to generate human-like conversation.
2. Content Generation: Content creators can guide LLMs using few-shot examples to produce articles, stories, and research summaries without extensive training data.
3. Question Answering Systems: In educational technology, few-shot learning can help develop systems that answer questions effectively based on limited curriculum examples.
4. Personalized Recommendations: E-commerce companies can use it to tailor product recommendations to individual preferences with minimum data.

Concluding Thoughts

Ready to implement your own chatbot?