from openai import OpenAI

client = OpenAI(
 api_key="your-crazyrouter-api-key",
 base_url="https://crazyrouter.com/v1"
)

# Generate embedding for a single text
response = client.embeddings.create(
 model="text-embedding-3-small",
 input="Machine learning is transforming industries worldwide."
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}") # Output: 1536
print(f"First 5 values: {embedding[:5]}")

import OpenAI from 'openai';

const client = new OpenAI({
 apiKey: 'your-crazyrouter-api-key',
 baseURL: 'https://crazyrouter.com/v1'
});

async function getEmbedding(text) {
 const response = await client.embeddings.create({
 model: 'text-embedding-3-small',
 input: text
 });

 return response.data[0].embedding;
}

// Usage
const embedding = await getEmbedding('Machine learning is amazing');
console.log(`Dimensions: ${embedding.length}`); // Output: 1536

curl -X POST https://crazyrouter.com/v1/embeddings \
 -H "Authorization: Bearer your-api-key" \
 -H "Content-Type: application/json" \
 -d '{
 "model": "text-embedding-3-small",
 "input": "Hello world"
 }'

{
 "object": "list",
 "model": "text-embedding-3-small",
 "usage": {
 "prompt_tokens": 2,
 "total_tokens": 2
 },
 "data": [
 {
 "object": "embedding",
 "index": 0,
 "embedding": [-0.0020785425, -0.049085874, 0.02094679, ...]
 }
 ]
}

from openai import OpenAI

client = OpenAI(
 api_key="your-crazyrouter-api-key",
 base_url="https://crazyrouter.com/v1"
)

# Batch embedding - multiple texts at once
texts = [
 "Python is a programming language",
 "JavaScript runs in browsers",
 "Machine learning uses neural networks"
]

response = client.embeddings.create(
 model="text-embedding-3-small",
 input=texts
)

# Access each embedding
for i, data in enumerate(response.data):
 print(f"Text {i}: {len(data.embedding)} dimensions")

# Output:
# Text 0: 1536 dimensions
# Text 1: 1536 dimensions
# Text 2: 1536 dimensions

# Use 512 dimensions instead of 1536
response = client.embeddings.create(
 model="text-embedding-3-small",
 input="Your text here",
 dimensions=512 # Options: 256, 512, 1024, 1536
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}") # Output: 512

import numpy as np
from openai import OpenAI

client = OpenAI(
 api_key="your-crazyrouter-api-key",
 base_url="https://crazyrouter.com/v1"
)

def get_embedding(text):
 """Get embedding for a single text"""
 response = client.embeddings.create(
 model="text-embedding-3-small",
 input=text
 )
 return response.data[0].embedding

def cosine_similarity(a, b):
 """Calculate cosine similarity between two vectors"""
 return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Document database
documents = [
 "Python is great for data science and machine learning",
 "JavaScript is essential for web development",
 "Docker containers simplify deployment",
 "Kubernetes orchestrates container workloads",
 "PostgreSQL is a powerful relational database"
]

# Pre-compute embeddings for all documents
doc_embeddings = [get_embedding(doc) for doc in documents]

# Search function
def search(query, top_k=3):
 query_embedding = get_embedding(query)

 # Calculate similarities
 similarities = [
 cosine_similarity(query_embedding, doc_emb)
 for doc_emb in doc_embeddings
 ]

 # Get top results
 results = sorted(
 zip(documents, similarities),
 key=lambda x: x[1],
 reverse=True
 )[:top_k]

 return results

# Example search
results = search("How to deploy applications?")
for doc, score in results:
 print(f"Score: {score:.4f} - {doc}")

# Output:
# Score: 0.8234 - Docker containers simplify deployment
# Score: 0.7891 - Kubernetes orchestrates container workloads
# Score: 0.6543 - PostgreSQL is a powerful relational database

import pinecone
from openai import OpenAI

# Initialize clients
client = OpenAI(
 api_key="your-crazyrouter-api-key",
 base_url="https://crazyrouter.com/v1"
)

pinecone.init(api_key="your-pinecone-key")
index = pinecone.Index("your-index")

def embed_and_upsert(texts, ids):
 """Embed texts and store in Pinecone"""
 response = client.embeddings.create(
 model="text-embedding-3-small",
 input=texts
 )

 vectors = [
 (id, data.embedding)
 for id, data in zip(ids, response.data)
 ]

 index.upsert(vectors=vectors)

def search_pinecone(query, top_k=5):
 """Search Pinecone with query embedding"""
 response = client.embeddings.create(
 model="text-embedding-3-small",
 input=query
 )

 results = index.query(
 vector=response.data[0].embedding,
 top_k=top_k
 )

 return results

import chromadb
from openai import OpenAI

client = OpenAI(
 api_key="your-crazyrouter-api-key",
 base_url="https://crazyrouter.com/v1"
)

# Initialize ChromaDB
chroma_client = chromadb.Client()
collection = chroma_client.create_collection("documents")

def get_embeddings(texts):
 """Get embeddings for multiple texts"""
 response = client.embeddings.create(
 model="text-embedding-3-small",
 input=texts
 )
 return [data.embedding for data in response.data]

# Add documents
documents = ["doc1 content", "doc2 content", "doc3 content"]
embeddings = get_embeddings(documents)

collection.add(
 embeddings=embeddings,
 documents=documents,
 ids=["doc1", "doc2", "doc3"]
)

# Query
query_embedding = get_embeddings(["search query"])[0]
results = collection.query(
 query_embeddings=[query_embedding],
 n_results=3
)

# Good - single API call for multiple texts
response = client.embeddings.create(
 model="text-embedding-3-small",
 input=["text1", "text2", "text3"] # Up to 2048 texts
)

# Bad - multiple API calls
for text in texts:
 response = client.embeddings.create(
 model="text-embedding-3-small",
 input=text
 )

import hashlib
import json

embedding_cache = {}

def get_embedding_cached(text):
 # Create cache key
 cache_key = hashlib.md5(text.encode()).hexdigest()

 if cache_key in embedding_cache:
 return embedding_cache[cache_key]

 response = client.embeddings.create(
 model="text-embedding-3-small",
 input=text
 )

 embedding = response.data[0].embedding
 embedding_cache[cache_key] = embedding

 return embedding