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..f49ea3048613
--- /dev/null
+++ b/docs/core_docs/docs/integrations/text_embedding/fireworks.ipynb
@@ -0,0 +1,222 @@
+{
+ "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": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "1:37 - Cannot find module '@langchain/community/embeddings/fireworks' or its corresponding type declarations.\n"
+ ]
+ }
+ ],
+ "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": null,
+ "id": "d817716b",
+ "metadata": {},
+ "outputs": [],
+ "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": null,
+ "id": "0d2befcd",
+ "metadata": {},
+ "outputs": [],
+ "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": null,
+ "id": "2f4d6e97",
+ "metadata": {},
+ "outputs": [],
+ "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": "TypeScript",
+ "language": "typescript",
+ "name": "tslab"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "mode": "typescript",
+ "name": "javascript",
+ "typescript": true
+ },
+ "file_extension": ".ts",
+ "mimetype": "text/typescript",
+ "name": "typescript",
+ "version": "3.7.2"
+ }
+ },
+ "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,
});