OpenAI Embeddings

pip install langchain-openai

export OPENAI_API_KEY="your-api-key"

from langchain_openai import OpenAIEmbeddings

embed = OpenAIEmbeddings(
    model="text-embedding-3-large"
)

input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])

[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

vectors = embed.embed_documents(["hello", "goodbye"])
print(len(vectors))
print(vectors[0][:3])

2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

vector = await embed.aembed_query(input_text)
print(vector[:3])

# Multiple documents
vectors = await embed.aembed_documents(["hello", "goodbye"])

embed = OpenAIEmbeddings(
    model="text-embedding-3-large",
    dimensions=1024
)

embed = OpenAIEmbeddings(
    model="text-embedding-3-large",
    dimensions=1024,
    chunk_size=1000,  # Max texts per batch
    max_retries=2,
    request_timeout=60.0,
    show_progress_bar=True
)

embed = OpenAIEmbeddings(
    model="your-model",
    base_url="https://your-api-endpoint.com",
    check_embedding_ctx_length=False
)