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pythonfrom openai import OpenAI import os client = OpenAI( base_url="https://fal.run/openrouter/router/openai/v1", api_key="not-needed", default_headers={ "Authorization": f"Key {os.environ['FAL_KEY']}", }, ) response = client.embeddings.create( model="openai/text-embedding-3-small", input="An AI that learns to dream in colors humanity has never seen." ) embedding = response.data[0].embedding print("Embedding length:", len(embedding)) # Multiple texts example (batch encoding) texts = [ "An AI that learns to dream.", "A robot that remembers forgotten memories.", "A neural network that writes its own mythology.", ] batch_response = client.embeddings.create( model="openai/text-embedding-3-small", input=texts, ) for i, item in enumerate(batch_response.data): print(f"Text {i} embedding length:", len(item.embedding))
pythonfrom openai import OpenAI import os import math client = OpenAI( base_url="https://fal.run/openrouter/router/openai/v1", api_key="not-needed", default_headers={ "Authorization": f"Key {os.environ['FAL_KEY']}", }, ) def cosine_sim(a, b): dot = sum(x * y for x, y in zip(a, b)) na = math.sqrt(sum(x * x for x in a)) nb = math.sqrt(sum(x * x for x in b)) return dot / (na * nb) texts = [ "An AI that learns to dream.", "A machine that hallucinates new worlds.", "A recipe for chocolate cake." ] resp = client.embeddings.create( model="openai/text-embedding-3-small", input=texts, ) emb_ai = resp.data[0].embedding emb_machine = resp.data[1].embedding emb_recipe = resp.data[2].embedding print("AI vs machine:", cosine_sim(emb_ai, emb_machine)) print("AI vs recipe:", cosine_sim(emb_ai, emb_recipe))
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