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

Overview​

Integration details​

Setup​

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

Credentials​

Head tofireworks.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

ifnot os.getenv("FIREWORKS_API_KEY"):
    os.environ["FIREWORKS_API_KEY"]= getpass.getpass("Enter your Fireworks API key: ")

To enable automated tracing of your model calls, set yourLangSmith API key:

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

Installation​

The LangChain Fireworks integration lives in thelangchain-fireworks package:

%pip install-qU langchain-fireworks

Instantiation​

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

from langchain_fireworksimport FireworksEmbeddings

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

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 ourRAG tutorials.

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

# Create a vector store with a sample text
from langchain_core.vectorstoresimport 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

'LangChain is the framework for building context-aware reasoning applications'

Direct Usage​

Under the hood, the vectorstore and retriever implementations are callingembeddings.embed_documents(...) andembeddings.embed_query(...) to create embeddings for the text(s) used infrom_texts and retrievalinvoke 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 withembed_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 withembed_documents:

text2=(
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors= embeddings.embed_documents([text, text2])
for vectorin 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 allFireworksEmbeddings features and configurations head to theAPI reference.

Related​

• Embedding modelconceptual guide
• Embedding modelhow-to guides