FireworksEmbeddings

class langchain_fireworks.embeddings.FireworksEmbeddings

Bases: BaseModel, Embeddings

Fireworks embedding model integration.

Setup:

Install langchain_fireworks and set environment variable FIREWORKS_API_KEY.

pip install -U langchain_fireworks
export FIREWORKS_API_KEY="your-api-key"

Key init args — completion params:

model: str

Name of Fireworks model to use.

Key init args — client params:

fireworks_api_key: SecretStr

Fireworks API key.

See full list of supported init args and their descriptions in the params section.

Instantiate:

from langchain_fireworks import FireworksEmbeddings

model = FireworksEmbeddings(
    model='nomic-ai/nomic-embed-text-v1.5'
    # Use FIREWORKS_API_KEY env var or pass it in directly
    # fireworks_api_key="..."
)

Embed multiple texts:

vectors = embeddings.embed_documents(['hello', 'goodbye'])
# Showing only the first 3 coordinates
print(len(vectors))
print(vectors[0][:3])

2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Embed single text:

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

[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param client: OpenAI = None

param fireworks_api_key: SecretStr [Optional] (alias 'api_key')

Fireworks API key.

Automatically read from env variable FIREWORKS_API_KEY if not provided.

param model: str = 'nomic-ai/nomic-embed-text-v1.5'

async aembed_documents(texts: list[str]) → list[list[float]]

Asynchronous Embed search docs.

Parameters:

texts (list[str]) – List of text to embed.

Returns:

List of embeddings.

Return type:

list[list[float]]

async aembed_query(text: str) → list[float]

Asynchronous Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

list[float]

embed_documents(texts: List[str]) → List[List[float]]

Embed search docs.

Parameters:

texts (List[str])

Return type:

List[List[float]]

embed_query(text: str) → List[float]

Embed query text.

Parameters:

text (str)

Return type:

List[float]