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FireworksEmbeddings

This will help you get started with Fireworks embedding models using LangChain. For detailed documentation on FireworksEmbeddings features and configuration options, please refer to the API reference.

Overview​

Integration details​

ProviderPackage
Fireworkslangchain-fireworks

Setup​

To access Fireworks embedding models you'll need to create a Fireworks account, get an API key, and install the langchain-fireworks integration package.

Credentials​

Head to fireworks.ai to sign up to Fireworks and generate an API key. Once you’ve done this set the FIREWORKS_API_KEY environment variable:

import getpass
import os

if not os.getenv("FIREWORKS_API_KEY"):
    os.environ["FIREWORKS_API_KEY"] = getpass.getpass("Enter your Fireworks API key: ")

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation​

The LangChain Fireworks integration lives in the langchain-fireworks package:

%pip install -qU langchain-fireworks

Instantiation​

Now we can instantiate our model object and generate chat completions:

from langchain_fireworks import FireworksEmbeddings

embeddings = FireworksEmbeddings(
    model="nomic-ai/nomic-embed-text-v1.5",
)
API Reference:FireworksEmbeddings

Indexing and Retrieval​

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the working with external knowledge tutorials.

Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore.

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content
API Reference:InMemoryVectorStore
'LangChain is the framework for building context-aware reasoning applications'

Direct Usage​

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

Embed single texts​

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
[0.01666259765625, 0.011688232421875, -0.1181640625, -0.10205078125, 0.05438232421875, -0.0890502929

Embed multiple texts​

You can embed multiple texts with embed_documents:

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector
[0.016632080078125, 0.01165008544921875, -0.1181640625, -0.10186767578125, 0.05438232421875, -0.0890
[-0.02667236328125, 0.036651611328125, -0.1630859375, -0.0904541015625, -0.022430419921875, -0.09545

API Reference​

For detailed documentation of all FireworksEmbeddings features and configurations head to the API reference.

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