Source code for langchain_fireworks.embeddings
from typing import Any, Dict, List
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.utils import secret_from_env
from openai import OpenAI # type: ignore
[docs]class FireworksEmbeddings(BaseModel, Embeddings):
"""Fireworks embedding model integration.
Setup:
Install ``langchain_fireworks`` and set environment variable
``FIREWORKS_API_KEY``.
.. code-block:: bash
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:
.. code-block:: python
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:
.. code-block:: python
vectors = embeddings.embed_documents(['hello', 'goodbye'])
# Showing only the first 3 coordinates
print(len(vectors))
print(vectors[0][:3])
.. code-block:: python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Embed single text:
.. code-block:: python
input_text = "The meaning of life is 42"
vector = embeddings.embed_query('hello')
print(vector[:3])
.. code-block:: python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
"""
_client: OpenAI = Field(default=None)
fireworks_api_key: SecretStr = Field(
alias="api_key",
default_factory=secret_from_env(
"FIREWORKS_API_KEY",
default="",
),
)
"""Fireworks API key.
Automatically read from env variable `FIREWORKS_API_KEY` if not provided.
"""
model: str = "nomic-ai/nomic-embed-text-v1.5"
@root_validator(pre=False, skip_on_failure=True)
def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate environment variables."""
values["_client"] = OpenAI(
api_key=values["fireworks_api_key"].get_secret_value(),
base_url="https://api.fireworks.ai/inference/v1",
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
return [
i.embedding
for i in self._client.embeddings.create(input=texts, model=self.model).data
]
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
return self.embed_documents([text])[0]