> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# FireworksEmbeddings integration
> Integrate with the FireworksEmbeddings embedding model using LangChain JavaScript.
This will help you get started with FireworksEmbeddings [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-fireworks/FireworksEmbeddings).
## Overview
### Integration details
| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/fireworks/) | Downloads | Version |
| :---------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------- | :---: | :--------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
| [`FireworksEmbeddings`](https://reference.langchain.com/javascript/langchain-fireworks/FireworksEmbeddings) | [`@langchain/fireworks`](https://www.npmjs.com/package/@langchain/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](https://fireworks.ai/) to sign up to `Fireworks` and generate an API key. Once you've done this set the `FIREWORKS_API_KEY` environment variable:
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export FIREWORKS_API_KEY="your-api-key"
```
If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below:
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```
### Installation
The LangChain `FireworksEmbeddings` integration lives in the `@langchain/fireworks` package:
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/fireworks @langchain/core
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/fireworks @langchain/core
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/fireworks @langchain/core
```
## Instantiation
Now we can instantiate our model object and embed text:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { FireworksEmbeddings } from "@langchain/fireworks";
const embeddings = new 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 our RAG tutorials under the [**Learn** tab](/oss/javascript/learn/).
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 using the demo [`MemoryVectorStore`](/oss/javascript/integrations/vectorstores/memory).
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// Create a vector store with a sample text
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";
const text = "LangChain is the framework for building context-aware reasoning applications";
const vectorstore = await MemoryVectorStore.fromDocuments(
[{ pageContent: text, metadata: {} }],
embeddings,
);
// Use the vector store as a retriever that returns a single document
const retriever = vectorstore.asRetriever(1);
// Retrieve the most similar text
const retrievedDocuments = await retriever.invoke("What is LangChain?");
retrievedDocuments[0].pageContent;
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
"LangChain is the framework for building context-aware reasoning applications"
```
## Direct usage
Under the hood, the vectorstore and retriever implementations are calling `embeddings.embedDocument(...)` and `embeddings.embedQuery(...)` to create embeddings for the text(s) used in `fromDocuments` and the retriever's `invoke` operations, respectively.
You can directly call these methods to get embeddings for your own use cases.
### Embed single texts
You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const singleVector = await embeddings.embedQuery(text);
console.log(singleVector.slice(0, 100));
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
0.01666259765625, 0.011688232421875, -0.1181640625,
-0.10205078125, 0.05438232421875, -0.08905029296875,
-0.018096923828125, 0.00952911376953125, -0.08056640625,
-0.0283050537109375, -0.01512908935546875, 0.0312042236328125,
0.08197021484375, 0.022552490234375, 0.0012683868408203125,
0.0133056640625, -0.04327392578125, -0.004322052001953125,
-0.02410888671875, -0.0012350082397460938, -0.04632568359375,
0.02996826171875, -0.0134124755859375, -0.037811279296875,
0.07672119140625, 0.021759033203125, 0.0179290771484375,
-0.0002741813659667969, -0.0582275390625, -0.0224456787109375,
0.0027675628662109375, -0.017425537109375, -0.01520538330078125,
-0.01146697998046875, -0.055267333984375, -0.083984375,
0.056793212890625, -0.003383636474609375, -0.034271240234375,
0.05108642578125, -0.01018524169921875, 0.0462646484375,
0.0012178421020507812, 0.005779266357421875, 0.0684814453125,
0.00797271728515625, -0.0176544189453125, 0.00257110595703125,
0.059539794921875, -0.06573486328125, -0.075439453125,
-0.0247344970703125, -0.0276947021484375, 0.003940582275390625,
0.02630615234375, 0.0660400390625, 0.0157470703125,
0.033050537109375, -0.0478515625, -0.03338623046875,
0.050384521484375, 0.07757568359375, -0.045166015625,
0.07586669921875, 0.0021915435791015625, 0.0237579345703125,
-0.052703857421875, 0.05023193359375, -0.0274810791015625,
-0.0025081634521484375, 0.019287109375, -0.03802490234375,
0.0216217041015625, 0.025360107421875, -0.04443359375,
-0.029205322265625, -0.002414703369140625, 0.027130126953125,
0.028961181640625, 0.078857421875, -0.0009660720825195312,
0.017608642578125, 0.05755615234375, -0.0285797119140625,
0.0039215087890625, -0.006908416748046875, -0.05364990234375,
-0.01342010498046875, -0.0247802734375, 0.08331298828125,
0.032928466796875, 0.00543975830078125, -0.0168304443359375,
-0.050018310546875, -0.05908203125, 0.031951904296875,
-0.0200347900390625, 0.019134521484375, -0.018035888671875,
-0.01178741455078125
]
```
### Embed multiple texts
You can embed multiple texts for indexing with `embedDocuments`. The internals used for this method may (but do not have to) differ from embedding queries:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs";
const vectors = await embeddings.embedDocuments([text, text2]);
console.log(vectors[0].slice(0, 100));
console.log(vectors[1].slice(0, 100));
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
0.016632080078125, 0.01165008544921875, -0.1181640625,
-0.10186767578125, 0.05438232421875, -0.08905029296875,
-0.0180511474609375, 0.00957489013671875, -0.08056640625,
-0.0283203125, -0.0151214599609375, 0.0311279296875,
0.08184814453125, 0.0225982666015625, 0.0012750625610351562,
0.01336669921875, -0.043365478515625, -0.004322052001953125,
-0.02410888671875, -0.0012559890747070312, -0.046356201171875,
0.0298919677734375, -0.013458251953125, -0.03765869140625,
0.07672119140625, 0.0217132568359375, 0.0179290771484375,
-0.0002269744873046875, -0.0582275390625, -0.0224609375,
0.002834320068359375, -0.0174407958984375, -0.01512908935546875,
-0.01146697998046875, -0.055206298828125, -0.08404541015625,
0.0567626953125, -0.0033092498779296875, -0.034271240234375,
0.05108642578125, -0.010101318359375, 0.046173095703125,
0.0011806488037109375, 0.005706787109375, 0.06854248046875,
0.0079193115234375, -0.0176239013671875, 0.002552032470703125,
0.059539794921875, -0.06573486328125, -0.07537841796875,
-0.02484130859375, -0.027740478515625, 0.003925323486328125,
0.0263671875, 0.0660400390625, 0.0156402587890625,
0.033050537109375, -0.047821044921875, -0.0333251953125,
0.050445556640625, 0.07757568359375, -0.045257568359375,
0.07586669921875, 0.0021724700927734375, 0.0237274169921875,
-0.052703857421875, 0.050323486328125, -0.0274658203125,
-0.0024662017822265625, 0.0194244384765625, -0.03802490234375,
0.02166748046875, 0.025360107421875, -0.044464111328125,
-0.0292816162109375, -0.0025119781494140625, 0.0271148681640625,
0.028961181640625, 0.078857421875, -0.0008907318115234375,
0.017669677734375, 0.0576171875, -0.0285797119140625,
0.0039825439453125, -0.00687408447265625, -0.0535888671875,
-0.0134735107421875, -0.0247650146484375, 0.0831298828125,
0.032989501953125, 0.005443572998046875, -0.0167999267578125,
-0.050018310546875, -0.059051513671875, 0.0318603515625,
-0.0200958251953125, 0.0191192626953125, -0.0180206298828125,
-0.01175689697265625
]
[
-0.02667236328125, 0.036651611328125, -0.1630859375,
-0.0904541015625, -0.022430419921875, -0.095458984375,
-0.037628173828125, 0.00473785400390625, -0.046051025390625,
0.0109710693359375, 0.0113525390625, 0.0254364013671875,
0.09368896484375, 0.0144195556640625, -0.007564544677734375,
-0.0014705657958984375, -0.0007691383361816406, -0.015716552734375,
-0.0242156982421875, -0.024871826171875, 0.00885009765625,
0.0012922286987304688, 0.023712158203125, -0.054595947265625,
0.06329345703125, 0.0289306640625, 0.0233612060546875,
-0.0374755859375, -0.0489501953125, -0.029510498046875,
0.0173492431640625, 0.0171356201171875, -0.01629638671875,
-0.0352783203125, -0.039398193359375, -0.11138916015625,
0.0296783447265625, -0.01467132568359375, 0.0009188652038574219,
0.048187255859375, -0.010650634765625, 0.03125,
0.005214691162109375, -0.015869140625, 0.06939697265625,
-0.0428466796875, 0.0266571044921875, 0.044189453125,
0.049957275390625, -0.054290771484375, 0.0107574462890625,
-0.03265380859375, -0.0109100341796875, -0.0144805908203125,
0.03936767578125, 0.07989501953125, -0.056976318359375,
0.0308380126953125, -0.035125732421875, -0.038848876953125,
0.10748291015625, 0.01129150390625, -0.0665283203125,
0.09710693359375, 0.03143310546875, -0.0104522705078125,
-0.062469482421875, 0.021148681640625, -0.00970458984375,
-0.06756591796875, 0.01019287109375, 0.00433349609375,
0.032928466796875, 0.020233154296875, -0.01336669921875,
-0.015472412109375, -0.0175933837890625, -0.0142364501953125,
-0.007450103759765625, 0.03759765625, 0.003551483154296875,
0.0069580078125, 0.042266845703125, -0.007488250732421875,
0.01922607421875, 0.007080078125, -0.0255889892578125,
-0.007686614990234375, -0.0848388671875, 0.058563232421875,
0.021148681640625, 0.034393310546875, 0.01087188720703125,
-0.0196380615234375, -0.09515380859375, 0.0054931640625,
-0.012481689453125, 0.003322601318359375, -0.019683837890625,
-0.0307159423828125
]
```
***
## API reference
For detailed documentation of all `FireworksEmbeddings` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-fireworks/FireworksEmbeddings).
***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/embeddings/fireworks.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).