Embeddings

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.023, -0.012, 0.045, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

from openai import OpenAI

# Initialize client pointing to NanoGPT
client = OpenAI(
    api_key="YOUR_NANOGPT_API_KEY",
    base_url="https://nano-gpt.com/api/v1"
)

# Create embedding
response = client.embeddings.create(
    input="Your text to embed",
    model="text-embedding-3-small"
)

# Access the embedding
embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")

import OpenAI from 'openai';

// Initialize client pointing to NanoGPT
const openai = new OpenAI({
    apiKey: 'YOUR_NANOGPT_API_KEY',
    baseURL: 'https://nano-gpt.com/api/v1'
});

// Create embedding
const response = await openai.embeddings.create({
    input: "Your text to embed",
    model: "text-embedding-3-small"
});

// Access the embedding
const embedding = response.data[0].embedding;
console.log(`Embedding dimensions: ${embedding.length}`);

curl https://nano-gpt.com/api/v1/embeddings \
  -H "Authorization: Bearer YOUR_NANOGPT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Your text to embed",
    "model": "text-embedding-3-small"
  }'

# Process multiple texts in a single request
texts = [
    "First text to embed",
    "Second text to embed",
    "Third text to embed"
]

response = client.embeddings.create(
    input=texts,  # Pass array of strings
    model="text-embedding-3-small"
)

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

# Reduce dimensions to 256 for faster similarity comparisons
response = client.embeddings.create(
    input="Your text to embed",
    model="text-embedding-3-small",
    dimensions=256  # Reduce from 1536 to 256
)

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Create embeddings for your documents
documents = ["Document 1 text", "Document 2 text", "Document 3 text"]
doc_embeddings = []

for doc in documents:
    response = client.embeddings.create(input=doc, model="text-embedding-3-small")
    doc_embeddings.append(response.data[0].embedding)

# Create embedding for search query
query = "Search query text"
query_response = client.embeddings.create(input=query, model="text-embedding-3-small")
query_embedding = query_response.data[0].embedding

# Calculate similarities
similarities = cosine_similarity([query_embedding], doc_embeddings)[0]

# Find most similar documents
top_matches = np.argsort(similarities)[::-1][:3]
for idx in top_matches:
    print(f"Document {idx}: {similarities[idx]:.3f} similarity")

# 1. Embed and store your knowledge base
knowledge_base = [
    {"text": "Fact 1...", "embedding": None},
    {"text": "Fact 2...", "embedding": None},
]

for item in knowledge_base:
    response = client.embeddings.create(
        input=item["text"], 
        model="text-embedding-3-small"
    )
    item["embedding"] = response.data[0].embedding

# 2. For a user query, find relevant context
user_query = "What is...?"
query_response = client.embeddings.create(
    input=user_query,
    model="text-embedding-3-small"
)
query_embedding = query_response.data[0].embedding

# 3. Find most relevant facts
# relevant_facts = find_similar_texts(query_embedding, knowledge_base, top_k=3)

# 4. Use retrieved context with chat completion
chat_response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": f"Context: {relevant_facts}"},
        {"role": "user", "content": user_query}
    ]
)

{
  "error": {
    "message": "Invalid model specified",
    "type": "invalid_request_error",
    "param": "model",
    "code": null
  }
}