AI/ML platforms and tools

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Hugging Face

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Key features

• 45,000+ models - Text, image, video, audio, and 3D modalities
• Unified API - One interface for models from OpenAI, Meta, Google, Anthropic, and more
• Inference API - Run models without managing infrastructure
• Free community tier - Test and prototype at no cost
• Paid compute - GPU instances starting at $0.60/hour
• Extensive libraries - Transformers, Diffusers, Tokenizers, TRL, PEFT

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Supported modalities

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Pricing

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Quick start examples

from transformers import pipeline

# Sentiment analysis
classifier = pipeline("sentiment-analysis")
result = classifier("I love this hackathon!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998}]

# Multi-class classification
classifier = pipeline("text-classification", 
                     model="distilbert-base-uncased-finetuned-sst-2-english")
results = classifier(["This is amazing!", "This is terrible."])
for r in results:
    print(f"{r['label']}: {r['score']:.4f}")

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Use cases

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Vector databases for RAG

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Pinecone (Cloud)

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Key features

• Automatic scaling - No infrastructure management required
• Multi-region deployment - Global apps with low latency
• Hybrid search - Combine sparse and dense vectors
• Enterprise compliance - SOC 2, GDPR certifications
• Sub-100ms latency - Even at massive scale
• Built-in metadata filtering - Combine vector search with traditional filters

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Pricing

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Quick start

import pinecone
from pinecone import Pinecone, ServerlessSpec

# Initialize
pc = Pinecone(api_key="YOUR_API_KEY")

# Create index
index = pc.create_index(
    name="hackathon-rag",
    dimension=1536,  # OpenAI embedding size
    metric="cosine",
    spec=ServerlessSpec(cloud="aws", region="us-east-1")
)

print("Index created successfully!")

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Why use Pinecone

• Handles scaling from thousands to billions of vectors
• Reliable for production with managed backups
• Perfect for customer-facing apps needing SLAs
• Automatic optimization and index management

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ChromaDB (Local)

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Key features

• Zero setup - pip install chromadb and start coding
• In-process - No separate server, zero network latency
• SQLite-based - Persistent storage to local disk
• Built-in embeddings - OpenAI, Sentence Transformers, and more
• Metadata filtering - Hybrid search capabilities
• Works offline - No internet required after installation

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Pricing

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Quick start

import chromadb
from chromadb.utils import embedding_functions

# Initialize client (persistent storage)
client = chromadb.PersistentClient(path="./chroma_db")

# Create collection with embedding function
openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="YOUR_OPENAI_KEY",
    model_name="text-embedding-ada-002"
)

collection = client.get_or_create_collection(
    name="hackathon_docs",
    embedding_function=openai_ef
)

print("Collection ready!")

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Why use ChromaDB

• Works offline (great for testing anywhere)
• In-process means zero network latency
• Easy to iterate on embedding strategies
• Handles up to ~100K vectors comfortably on local machines

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Pinecone vs ChromaDB comparison

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When to use what

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Langflow

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Key features

• Visual drag-and-drop - Build workflows with a flowchart interface
• Python-based - Agnostic to any model, API, or database
• Instant API deployment - Turn your flow into an endpoint with one click
• Pre-built components - Agents, RAG, vector stores, tools, chains
• Multi-agent orchestration - Coordinate multiple AI agents
• Free cloud service - Get started in minutes without local setup

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Architecture

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Available components

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API usage example

import requests

response = requests.post(
    "https://your-langflow-api.com/run",
    json={
        "input": "What are the best practices for RAG systems?",
        "tweaks": {}  # Optional parameter overrides
    }
)

result = response.json()
print(result["output"])

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Use cases

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When to use what

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Best practices

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Model selection

1. Text tasks: Start with DistilBERT or BERT-base before jumping to GPT-4
2. Image generation: Use Stable Diffusion 2.1 (free) before DALL-E 3 (paid)
3. Embeddings: OpenAI ada-002 is reliable, but Sentence Transformers are free

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RAG system tips

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Cost optimization

1. Use ChromaDB for development - Free, no API costs
2. Batch embedding calls - Process multiple texts in one API request
3. Cache everything - Store embeddings, search results, and LLM responses
4. Set usage limits - Implement hard limits on API calls during demos
5. Monitor spending - Track costs daily during hackathons

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Example: Complete RAG system

import chromadb
from chromadb.utils import embedding_functions
from transformers import pipeline

# Initialize ChromaDB with free embeddings
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
    model_name="all-MiniLM-L6-v2"
)

client = chromadb.PersistentClient(path="./rag_db")
collection = client.get_or_create_collection(
    name="knowledge_base",
    embedding_function=sentence_transformer_ef
)

# Add documents
docs = [
    "RAG systems combine retrieval with generation for better LLM responses.",
    "Vector databases store embeddings for semantic similarity search.",
    "ChromaDB is a free, open-source vector database perfect for prototyping."
]

collection.add(
    documents=docs,
    ids=[f"doc{i}" for i in range(len(docs))]
)

# Initialize QA model from Hugging Face (free)
qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")

def rag_query(question):
    # Retrieve relevant context
    results = collection.query(
        query_texts=[question],
        n_results=2
    )
    
    context = " ".join(results['documents'][0])
    
    # Generate answer
    answer = qa_model(question=question, context=context)
    
    return {
        'answer': answer['answer'],
        'confidence': answer['score'],
        'sources': results['documents'][0]
    }

# Test the RAG system
result = rag_query("What is ChromaDB?")
print(f"Answer: {result['answer']}")
print(f"Confidence: {result['confidence']:.2%}")
print(f"Sources: {result['sources']}")