diff --git a/docs/core_docs/docs/integrations/text_embedding/fireworks.ipynb b/docs/core_docs/docs/integrations/text_embedding/fireworks.ipynb
new file mode 100644
index 000000000000..2ca95c5bd420
--- /dev/null
+++ b/docs/core_docs/docs/integrations/text_embedding/fireworks.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "afaf8039",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "---\n",
+ "sidebar_label: Fireworks\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a3d6f34",
+ "metadata": {},
+ "source": [
+ "# FireworksEmbeddings\n",
+ "\n",
+ "This will help you get started with FireworksEmbeddings [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_community_embeddings_fireworks.FireworksEmbeddings.html).\n",
+ "\n",
+ "## Overview\n",
+ "### Integration details\n",
+ "\n",
+ "| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/text_embedding/fireworks/) | Package downloads | Package latest |\n",
+ "| :--- | :--- | :---: | :---: | :---: | :---: |\n",
+ "| [FireworksEmbeddings](https://api.js.langchain.com/classes/langchain_community_embeddings_fireworks.FireworksEmbeddings.html) | [@langchain/community](https://api.js.langchain.com/modules/langchain_community_embeddings_fireworks.html) | ❌ | ✅ |  |  |\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "To access Fireworks embedding models you'll need to create a Fireworks account, get an API key, and install the `@langchain/community` integration package.\n",
+ "\n",
+ "### Credentials\n",
+ "\n",
+ "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:\n",
+ "\n",
+ "```bash\n",
+ "export FIREWORKS_API_KEY=\"your-api-key\"\n",
+ "```\n",
+ "\n",
+ "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n",
+ "\n",
+ "```bash\n",
+ "# export LANGCHAIN_TRACING_V2=\"true\"\n",
+ "# export LANGCHAIN_API_KEY=\"your-api-key\"\n",
+ "```\n",
+ "\n",
+ "### Installation\n",
+ "\n",
+ "The LangChain `FireworksEmbeddings` integration lives in the `@langchain/community` package:\n",
+ "\n",
+ "```{=mdx}\n",
+ "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+ "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " @langchain/community\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45dd1724",
+ "metadata": {},
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "Now we can instantiate our model object and generate chat completions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9ea7a09b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import { FireworksEmbeddings } from \"@langchain/community/embeddings/fireworks\";\n",
+ "\n",
+ "const embeddings = new FireworksEmbeddings({\n",
+ " modelName: \"nomic-ai/nomic-embed-text-v1.5\",\n",
+ "});"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77d271b6",
+ "metadata": {},
+ "source": [
+ "## Indexing and Retrieval\n",
+ "\n",
+ "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](/docs/tutorials/#working-with-external-knowledge).\n",
+ "\n",
+ "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`](/docs/integrations/vectorstores/memory)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d817716b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[32m\"LangChain is the framework for building context-aware reasoning applications\"\u001b[39m"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "// Create a vector store with a sample text\n",
+ "import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
+ "\n",
+ "const text = \"LangChain is the framework for building context-aware reasoning applications\";\n",
+ "\n",
+ "const vectorstore = await MemoryVectorStore.fromDocuments(\n",
+ " [{ pageContent: text, metadata: {} }],\n",
+ " embeddings,\n",
+ ");\n",
+ "\n",
+ "// Use the vector store as a retriever that returns a single document\n",
+ "const retriever = vectorstore.asRetriever(1);\n",
+ "\n",
+ "// Retrieve the most similar text\n",
+ "const retrievedDocuments = await retriever.invoke(\"What is LangChain?\");\n",
+ "\n",
+ "retrievedDocuments[0].pageContent;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e02b9855",
+ "metadata": {},
+ "source": [
+ "## Direct Usage\n",
+ "\n",
+ "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.\n",
+ "\n",
+ "You can directly call these methods to get embeddings for your own use cases.\n",
+ "\n",
+ "### Embed single texts\n",
+ "\n",
+ "You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0d2befcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\n",
+ " 0.01666259765625, 0.011688232421875, -0.1181640625,\n",
+ " -0.10205078125, 0.05438232421875, -0.08905029296875,\n",
+ " -0.018096923828125, 0.00952911376953125, -0.08056640625,\n",
+ " -0.0283050537109375, -0.01512908935546875, 0.0312042236328125,\n",
+ " 0.08197021484375, 0.022552490234375, 0.0012683868408203125,\n",
+ " 0.0133056640625, -0.04327392578125, -0.004322052001953125,\n",
+ " -0.02410888671875, -0.0012350082397460938, -0.04632568359375,\n",
+ " 0.02996826171875, -0.0134124755859375, -0.037811279296875,\n",
+ " 0.07672119140625, 0.021759033203125, 0.0179290771484375,\n",
+ " -0.0002741813659667969, -0.0582275390625, -0.0224456787109375,\n",
+ " 0.0027675628662109375, -0.017425537109375, -0.01520538330078125,\n",
+ " -0.01146697998046875, -0.055267333984375, -0.083984375,\n",
+ " 0.056793212890625, -0.003383636474609375, -0.034271240234375,\n",
+ " 0.05108642578125, -0.01018524169921875, 0.0462646484375,\n",
+ " 0.0012178421020507812, 0.005779266357421875, 0.0684814453125,\n",
+ " 0.00797271728515625, -0.0176544189453125, 0.00257110595703125,\n",
+ " 0.059539794921875, -0.06573486328125, -0.075439453125,\n",
+ " -0.0247344970703125, -0.0276947021484375, 0.003940582275390625,\n",
+ " 0.02630615234375, 0.0660400390625, 0.0157470703125,\n",
+ " 0.033050537109375, -0.0478515625, -0.03338623046875,\n",
+ " 0.050384521484375, 0.07757568359375, -0.045166015625,\n",
+ " 0.07586669921875, 0.0021915435791015625, 0.0237579345703125,\n",
+ " -0.052703857421875, 0.05023193359375, -0.0274810791015625,\n",
+ " -0.0025081634521484375, 0.019287109375, -0.03802490234375,\n",
+ " 0.0216217041015625, 0.025360107421875, -0.04443359375,\n",
+ " -0.029205322265625, -0.002414703369140625, 0.027130126953125,\n",
+ " 0.028961181640625, 0.078857421875, -0.0009660720825195312,\n",
+ " 0.017608642578125, 0.05755615234375, -0.0285797119140625,\n",
+ " 0.0039215087890625, -0.006908416748046875, -0.05364990234375,\n",
+ " -0.01342010498046875, -0.0247802734375, 0.08331298828125,\n",
+ " 0.032928466796875, 0.00543975830078125, -0.0168304443359375,\n",
+ " -0.050018310546875, -0.05908203125, 0.031951904296875,\n",
+ " -0.0200347900390625, 0.019134521484375, -0.018035888671875,\n",
+ " -0.01178741455078125\n",
+ "]\n"
+ ]
+ }
+ ],
+ "source": [
+ "const singleVector = await embeddings.embedQuery(text);\n",
+ "\n",
+ "console.log(singleVector.slice(0, 100));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b5a7d03",
+ "metadata": {},
+ "source": [
+ "### Embed multiple texts\n",
+ "\n",
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2f4d6e97",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\n",
+ " 0.016632080078125, 0.01165008544921875, -0.1181640625,\n",
+ " -0.10186767578125, 0.05438232421875, -0.08905029296875,\n",
+ " -0.0180511474609375, 0.00957489013671875, -0.08056640625,\n",
+ " -0.0283203125, -0.0151214599609375, 0.0311279296875,\n",
+ " 0.08184814453125, 0.0225982666015625, 0.0012750625610351562,\n",
+ " 0.01336669921875, -0.043365478515625, -0.004322052001953125,\n",
+ " -0.02410888671875, -0.0012559890747070312, -0.046356201171875,\n",
+ " 0.0298919677734375, -0.013458251953125, -0.03765869140625,\n",
+ " 0.07672119140625, 0.0217132568359375, 0.0179290771484375,\n",
+ " -0.0002269744873046875, -0.0582275390625, -0.0224609375,\n",
+ " 0.002834320068359375, -0.0174407958984375, -0.01512908935546875,\n",
+ " -0.01146697998046875, -0.055206298828125, -0.08404541015625,\n",
+ " 0.0567626953125, -0.0033092498779296875, -0.034271240234375,\n",
+ " 0.05108642578125, -0.010101318359375, 0.046173095703125,\n",
+ " 0.0011806488037109375, 0.005706787109375, 0.06854248046875,\n",
+ " 0.0079193115234375, -0.0176239013671875, 0.002552032470703125,\n",
+ " 0.059539794921875, -0.06573486328125, -0.07537841796875,\n",
+ " -0.02484130859375, -0.027740478515625, 0.003925323486328125,\n",
+ " 0.0263671875, 0.0660400390625, 0.0156402587890625,\n",
+ " 0.033050537109375, -0.047821044921875, -0.0333251953125,\n",
+ " 0.050445556640625, 0.07757568359375, -0.045257568359375,\n",
+ " 0.07586669921875, 0.0021724700927734375, 0.0237274169921875,\n",
+ " -0.052703857421875, 0.050323486328125, -0.0274658203125,\n",
+ " -0.0024662017822265625, 0.0194244384765625, -0.03802490234375,\n",
+ " 0.02166748046875, 0.025360107421875, -0.044464111328125,\n",
+ " -0.0292816162109375, -0.0025119781494140625, 0.0271148681640625,\n",
+ " 0.028961181640625, 0.078857421875, -0.0008907318115234375,\n",
+ " 0.017669677734375, 0.0576171875, -0.0285797119140625,\n",
+ " 0.0039825439453125, -0.00687408447265625, -0.0535888671875,\n",
+ " -0.0134735107421875, -0.0247650146484375, 0.0831298828125,\n",
+ " 0.032989501953125, 0.005443572998046875, -0.0167999267578125,\n",
+ " -0.050018310546875, -0.059051513671875, 0.0318603515625,\n",
+ " -0.0200958251953125, 0.0191192626953125, -0.0180206298828125,\n",
+ " -0.01175689697265625\n",
+ "]\n",
+ "[\n",
+ " -0.02667236328125, 0.036651611328125, -0.1630859375,\n",
+ " -0.0904541015625, -0.022430419921875, -0.095458984375,\n",
+ " -0.037628173828125, 0.00473785400390625, -0.046051025390625,\n",
+ " 0.0109710693359375, 0.0113525390625, 0.0254364013671875,\n",
+ " 0.09368896484375, 0.0144195556640625, -0.007564544677734375,\n",
+ " -0.0014705657958984375, -0.0007691383361816406, -0.015716552734375,\n",
+ " -0.0242156982421875, -0.024871826171875, 0.00885009765625,\n",
+ " 0.0012922286987304688, 0.023712158203125, -0.054595947265625,\n",
+ " 0.06329345703125, 0.0289306640625, 0.0233612060546875,\n",
+ " -0.0374755859375, -0.0489501953125, -0.029510498046875,\n",
+ " 0.0173492431640625, 0.0171356201171875, -0.01629638671875,\n",
+ " -0.0352783203125, -0.039398193359375, -0.11138916015625,\n",
+ " 0.0296783447265625, -0.01467132568359375, 0.0009188652038574219,\n",
+ " 0.048187255859375, -0.010650634765625, 0.03125,\n",
+ " 0.005214691162109375, -0.015869140625, 0.06939697265625,\n",
+ " -0.0428466796875, 0.0266571044921875, 0.044189453125,\n",
+ " 0.049957275390625, -0.054290771484375, 0.0107574462890625,\n",
+ " -0.03265380859375, -0.0109100341796875, -0.0144805908203125,\n",
+ " 0.03936767578125, 0.07989501953125, -0.056976318359375,\n",
+ " 0.0308380126953125, -0.035125732421875, -0.038848876953125,\n",
+ " 0.10748291015625, 0.01129150390625, -0.0665283203125,\n",
+ " 0.09710693359375, 0.03143310546875, -0.0104522705078125,\n",
+ " -0.062469482421875, 0.021148681640625, -0.00970458984375,\n",
+ " -0.06756591796875, 0.01019287109375, 0.00433349609375,\n",
+ " 0.032928466796875, 0.020233154296875, -0.01336669921875,\n",
+ " -0.015472412109375, -0.0175933837890625, -0.0142364501953125,\n",
+ " -0.007450103759765625, 0.03759765625, 0.003551483154296875,\n",
+ " 0.0069580078125, 0.042266845703125, -0.007488250732421875,\n",
+ " 0.01922607421875, 0.007080078125, -0.0255889892578125,\n",
+ " -0.007686614990234375, -0.0848388671875, 0.058563232421875,\n",
+ " 0.021148681640625, 0.034393310546875, 0.01087188720703125,\n",
+ " -0.0196380615234375, -0.09515380859375, 0.0054931640625,\n",
+ " -0.012481689453125, 0.003322601318359375, -0.019683837890625,\n",
+ " -0.0307159423828125\n",
+ "]\n"
+ ]
+ }
+ ],
+ "source": [
+ "const text2 = \"LangGraph is a library for building stateful, multi-actor applications with LLMs\";\n",
+ "\n",
+ "const vectors = await embeddings.embedDocuments([text, text2]);\n",
+ "\n",
+ "console.log(vectors[0].slice(0, 100));\n",
+ "console.log(vectors[1].slice(0, 100));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8938e581",
+ "metadata": {},
+ "source": [
+ "## API reference\n",
+ "\n",
+ "For detailed documentation of all FireworksEmbeddings features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_embeddings_fireworks.FireworksEmbeddings.html"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Deno",
+ "language": "typescript",
+ "name": "deno"
+ },
+ "language_info": {
+ "file_extension": ".ts",
+ "mimetype": "text/x.typescript",
+ "name": "typescript",
+ "nb_converter": "script",
+ "pygments_lexer": "typescript",
+ "version": "5.3.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/core_docs/docs/integrations/text_embedding/fireworks.mdx b/docs/core_docs/docs/integrations/text_embedding/fireworks.mdx
deleted file mode 100644
index f1cb9e6f55dc..000000000000
--- a/docs/core_docs/docs/integrations/text_embedding/fireworks.mdx
+++ /dev/null
@@ -1,29 +0,0 @@
----
-sidebar_label: Fireworks
----
-
-import CodeBlock from "@theme/CodeBlock";
-
-# Fireworks
-
-The `FireworksEmbeddings` class allows you to use the Fireworks AI API to generate embeddings.
-
-## Setup
-
-First, sign up for a [Fireworks API key](https://fireworks.ai/) and set it as an environment variable called `FIREWORKS_API_KEY`.
-
-Next, install the `@langchain/community` package as shown below:
-
-import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
-
-
-
-```bash npm2yarn
-npm install @langchain/community
-```
-
-## Usage
-
-import FireworksExample from "@examples/models/embeddings/fireworks.ts";
-
-{FireworksExample}
diff --git a/libs/langchain-community/src/embeddings/fireworks.ts b/libs/langchain-community/src/embeddings/fireworks.ts
index 8a6d77c4a669..05d928f10b3b 100644
--- a/libs/langchain-community/src/embeddings/fireworks.ts
+++ b/libs/langchain-community/src/embeddings/fireworks.ts
@@ -7,7 +7,11 @@ import { chunkArray } from "@langchain/core/utils/chunk_array";
* parameters specific to the FireworksEmbeddings class.
*/
export interface FireworksEmbeddingsParams extends EmbeddingsParams {
+ /**
+ * @deprecated Use `model` instead.
+ */
modelName: string;
+ model: string;
/**
* The maximum number of documents to embed in a single request. This is
@@ -41,8 +45,13 @@ export class FireworksEmbeddings
extends Embeddings
implements FireworksEmbeddingsParams
{
+ /**
+ * @deprecated Use `model` instead.
+ */
modelName = "nomic-ai/nomic-embed-text-v1.5";
+ model = "nomic-ai/nomic-embed-text-v1.5";
+
batchSize = 8;
private apiKey: string;
@@ -74,7 +83,8 @@ export class FireworksEmbeddings
throw new Error("Fireworks AI API key not found");
}
- this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
+ this.model = fieldsWithDefaults?.model ?? this.model;
+ this.modelName = this.model;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.apiKey = apiKey;
this.apiUrl = `${this.basePath}/embeddings`;
@@ -90,7 +100,7 @@ export class FireworksEmbeddings
const batchRequests = batches.map((batch) =>
this.embeddingWithRetry({
- model: this.modelName,
+ model: this.model,
input: batch,
})
);
@@ -117,7 +127,7 @@ export class FireworksEmbeddings
*/
async embedQuery(text: string): Promise {
const { data } = await this.embeddingWithRetry({
- model: this.modelName,
+ model: this.model,
input: text,
});