few-shots 0.3.0

Take your best shot

🎯 FewShots: The best few shots with LLMs

Ever wished your AI model had a better memory? Meet FewShot - the simple yet powerful library for managing and retrieving few-shot examples with style! 🧠✨

🌟 Features

• 🎮 Easy to Use: Simple, intuitive API for managing your AI's example database
• 🔄 Structured Output: Support for structured outputs

💡 Use Cases

• 🤖 Enhance your chatbot with dynamic example retrieval
• 📚 Build a self-improving knowledge base
• 🎯 Implement context-aware few-shot learning

🛠️ Core Components

• Shot: The fundamental unit representing an input-output pair with a unique ID (bring your own ID or let FewShots hash the inputs)
• Embed: Converts inputs into vector embeddings for similarity search
• Store: Manages storage and retrieval of examples
• Client: Ties everything together with a clean, simple interface

🚀 Quick Start

from sentence_transformers import SentenceTransformer # Can also use OpenAI, etc.
from few_shots.client import FewShots
from few_shots.embed.transformers import TransformersEmbed
from few_shots.store.memory import MemoryStore

# Create a FewShot client
shots = FewShots(
    embed=TransformersEmbed(SentenceTransformer("all-MiniLM-L6-v2")),
    store=MemoryStore()
)

# Add some examples
shots.add(
    inputs="How do I make a pizza?",
    outputs="1. Make the dough 2. Add toppings 3. Bake at 450°F"
)

# Find similar examples
best_shots = shots.list("What's the recipe for pizza?", limit=1)
for distance, shot in results:
    print(f"Found match (distance: {distance:.2f}):")
    print(f"Q: {shot.inputs}")
    print(f"A: {shot.outputs}")

# Use with your LLM
from few_shots.utils.format import shots_to_messages

openai.chat.completions.create(
    ...,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        *shots_to_messages(best_shots),
        {"role": "user", "content": "What's the recipe for pizza?"},
    ]
)

🔧 Installation

pip install few-shots
rye add few-shots
poetry add few-shots

🎮 Usage Examples

Working with Structured Output I/O

# Add structured data
shots.add(
    inputs={"type": "greeting", "language": "English"},
    outputs={"text": "Hello, world!"}
)

# Search with similar inputs
best_shots = shots.list({"type": "greeting", "language": "English"})

Async Support

from few_shots.async_client import AsyncFewShot

shots = AsyncFewShots(embed=async_embedder, store=async_store)

# Add examples asynchronously
await shots.add(
    inputs="What's the weather like?",
    outputs="I don't have access to real-time weather data."
)

# Search asynchronously
best_shots = await shots.list("How's the weather today?", limit=1)

Using OpenAI / LiteLLM for Embeddings

The OpenAIEmbed and AsyncOpenAIEmbed classes are compatible with all OpenAI-compatible SDKs.

from few_shots import AsyncFewShots
from few_shots.embed.openai import OpenAIEmbed, AsyncOpenAIEmbed # Compatible with all OpenAI

from openai import OpenAI

shots = FewShots(
    embed=OpenAIEmbed(
        OpenAI().embeddings.create,
        model="...",
        **kwargs,
    ),
    store=MemoryStore()
)

from litellm import aembedding

shots = AsyncFewShots(
    embed=AsyncOpenAIEmbed(
        aembedding,
        model="...",
        **kwargs,
    ),
    store=MemoryStore()
)

Using different Vector Stores

from few_shots.store.pg import PGStore, AsyncPGStore
from few_shots.store.chroma import ChromaStore, AsyncChromaStore
from few_shots.store.qdrant import QdrantStore, AsyncQdrantStore
from few_shots.store.weaviate import WeaviateStore, AsyncWeaviateStore
from few_shots.store.turbopuffer import TurboPufferStore # Untested
from few_shots.store.milvus import MilvusStore # TODO

# check out the store's .setup method to see how to configure it
# this method creates the table, collection, indexes, etc. and is idempotent

🤝 Contributing

We love contributions! Feel free to:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

📝 License

MIT License - feel free to use it in your projects!

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Made with ❤️ by developers who believe in the power of learning from examples.

Remember: The best AI is the one that learns from experience! 🌟