OpenRouter Embeddings [OpenAI Compatible] | Large Language Models

🧩 Usage with OpenAI Client (Embeddings API)

python
from 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))

🧪 Comparing Embeddings (Simple Similarity Example)

python
from 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))

📚 Documentation

For more details, visit the official docs:

🔗 OpenRouter API Docs – https://openrouter.ai/docs/quickstart
⚡ fal.ai API Docs – https://docs.fal.ai/model-apis

openrouter/router/openai/v1/embeddings

Input

Result

Logs

🧩 Usage with OpenAI Client (Embeddings API)

🧪 Comparing Embeddings (Simple Similarity Example)

📚 Documentation